JP7802592B2 - Air conditioning system - Google Patents

Description

The present invention relates to an air conditioning system that provides air conditioning particularly for the perimeter zone.

When providing health air conditioning primarily for people in an office building or other building, the thermal conditions vary from one location to another within the building. For example, perimeter zones, such as those near windows, are more susceptible to the effects of exterior loads than the interior zones within, resulting in significantly different heat inputs between rooms on the south side, which are more susceptible to sunlight, and rooms on the north side, which are shaded even during the day. Even if the amount of heat input from outside is the same, the thermal load may differ from room to room due to the exhaust heat from indoor equipment. In order to accommodate these different thermal conditions and maintain optimal air conditions in each area, variable air volume single-duct air conditioning or per-floor unit systems, for example, involve installing multiple variable air volume devices in the target space and placing temperature sensors near them. The air volume of each variable air volume device and the temperature of the air supplied by the air conditioner are then adjusted based on the temperature sensor readings.

Here, temperature sensors are generally installed on or near the ceiling, especially in air conditioning systems intended for large-span spaces. In the old days, when there were many pillars, it was easy to install temperature sensors at the same level as the occupied area, but this is no longer possible. This is because people and equipment are often located near the floor and would get in the way. As a result, the indoor temperature detected by the air conditioning system can differ from the temperature actually felt by people and equipment in the occupied area near the floor. In particular, during seasons when the outside temperature is low, the air in the perimeter zone, especially near the inside of windows, is cooled by the outside air, creating a current of cold air that sinks downward and flows along the floor, known as a cold draft. This can make people in the perimeter zone or nearby areas feel uncomfortable. Warm air has a lower specific gravity than cold air, so even if warm air is being supplied from the ceiling outlet, if there is a large temperature difference between the warm air and the cold air caused by cooling from outside air, or if the air volume is insufficient, the inertial force of the air blowing downward from the outlet will be canceled out by the upward buoyancy, and the air will not reach the floor. In other words, in seasons when the outside temperature is low, at foot level in the perimeter zone, for example, cold air will prevail over the warm air supplied from the variable air volume device, and it is possible that the supply of warm air to the occupied area will be insufficient.

One effective solution to this situation is to install a ventilation device known as a perimeter fan or air barrier fan, separate from the ceiling-mounted air outlet that supplies the perimeter zone with intake air controlled by the variable air volume device. Perimeter fans are installed, for example, near the floor near the windows in the perimeter zone, creating an upward airflow from there. This prevents indoor air from being cooled near the windows, creating a cold draft that then flows along the floor into the room. Furthermore, by sending warm air controlled by the variable air volume device from the ceiling-mounted air outlet toward the perimeter fan, warm air can be supplied to the outer edges of the perimeter zone, maintaining a comfortable air environment for people in the living area.

However, even with such a perimeter fan, it may not be sufficient to address the aforementioned cold drafts or insufficient supply of warm air to the occupied area. Since a temperature sensor for measuring indoor air temperature is installed near the ceiling, the measured value may differ from the air temperature near the floor. Therefore, if the air conditioning system is operated based on the temperature sensor's measurement, it is possible that sufficient warm air will not be supplied to the perimeter zone. For example, if the air outlet controlled by the variable air volume device is unable to supply a sufficient amount of warm air to the perimeter fan, or if there is a temperature difference between the upper and lower air in the room, the temperature measured by the temperature sensor near the ceiling will be higher than the temperature near the floor in the perimeter zone. As a result, the actual temperature near the floor near the perimeter zone may not be reflected in the operation of the variable air volume device, and even if a comfortable temperature is maintained near the ceiling, the temperature in the occupied zone may remain low.
One possible solution to this situation would be to simply raise the set temperature on the variable air volume device, but if this were to be done, there would be a concern that the temperature in the occupied area would rise too much when, for example, the outside temperature rises as the season changes from winter to mid-season and the temperature difference between the top and bottom of the room becomes smaller.

For example, Patent Document 1 below describes technology that can address these issues.

Japanese Patent Application Laid-Open No. 2021-76348

The technology described in Patent Document 1 estimates the temperature near the floor in the perimeter zone and adjusts the supply air volume of the variable air volume device based on this. By estimating the temperature near the floor instead of the measurement value from a temperature sensor installed at ceiling height and using this value to operate the air conditioning system, it is possible to operate the system in accordance with the conditions near the floor.

Here, an estimated value is used as the temperature value near the floor instead of the value measured by a temperature sensor actually installed in that position, and it is of course desirable for this estimated value to be as close to the actual value as possible. Even if an estimated temperature near the floor, which is close to the occupied area, is used instead of the measured temperature near the ceiling, if the estimated temperature deviates from the actual value, the same problems as above will ultimately occur, or conversely, there may be cases where excessive warm air is supplied.

In light of this situation, the present invention aims to provide an air conditioning system that can appropriately estimate the air temperature near the floor in the perimeter zone.

The present invention includes an air conditioner that delivers supply air;
an air supply duct that guides air from the air conditioner to a target space;
A plurality of air outlets that blow supply air into the target space;
an air volume variable device for adjusting the volume of supply air blown out from one of the air outlets that supplies air to the perimeter zone;
a perimeter fan provided in the perimeter zone for blowing indoor air upward;
an intake port provided above the perimeter fan for drawing in at least a portion of return air to the air conditioner;
a ceiling temperature sensor for measuring a ceiling temperature in the perimeter zone, and estimating the near-floor temperature at each position in at least different directions in the perimeter zone based on a parameter related to the near-floor temperature in the perimeter zone;
The airflow rate of the variable air volume device facing the perimeter zone is adjusted based on the estimated near-floor temperature,
the estimation of the near-floor temperature is configured to be performed using at least the ceiling temperature sensed by the ceiling temperature sensor as a parameter , and is further configured to be performed using the amount of solar radiation as a parameter;
The amount of solar radiation is grasped as an estimated value calculated based on the amount of extra-atmospheric global solar radiation during a shadow time set as a time when a shadow is cast on the pyranometer, while
At other times, the air conditioning system is characterized in that it is configured to grasp the actual measured value of the pyranometer .
The present invention also provides an air conditioner that delivers supply air;
an air supply duct that guides air from the air conditioner to a target space;
A plurality of air outlets that blow supply air into the target space;
an air volume variable device for adjusting the volume of supply air blown out from one of the air outlets that supplies air to the perimeter zone;
a perimeter fan provided in the perimeter zone for blowing indoor air upward;
an intake port provided above the perimeter fan for drawing in at least a portion of return air to the air conditioner;
a ceiling temperature sensor for measuring the ceiling temperature in the perimeter zone;
The system is configured to estimate near-floor temperatures at least at positions in different directions in the perimeter zone based on parameters related to near-floor temperatures in the perimeter zone, and adjust the blow-out air volume of the variable air volume device facing the perimeter zone based on the estimated near-floor temperatures;
the estimation of the near-floor temperature is configured to be performed using at least the ceiling temperature detected by the ceiling temperature sensor as a parameter, and is configured to be performed using the presence or absence of solar radiation at the target position as a further parameter;
The presence or absence of solar radiation is determined based on the shadow time at the target location.
This relates to an air conditioning system characterized by the above.

The air conditioning system of the present invention can be configured to estimate the near-floor temperature using at least past values of the ceiling temperature detected by the ceiling temperature sensor as parameters.

The air conditioning system of the present invention can be configured to estimate the near-floor temperature using the amount of solar radiation as an additional parameter, and the amount of solar radiation can be calculated as an estimated value based on extra-atmospheric global solar radiation during the shadow time set as the time when a shadow is cast on the pyranometer, while at other times it can be calculated as an actual measured value from the pyranometer.

In the air conditioning system of the present invention, the estimated amount of solar radiation during shadow hours can be calculated by multiplying the extra-atmospheric global solar radiation during shadow hours by a preset coefficient.

In the air conditioning system of the present invention, the estimated value of solar radiation during the shadow period can be calculated by multiplying the amount of extraterrestrial global solar radiation during the shadow period by a coefficient calculated by dividing the actual measured value of solar radiation before the shadow period by the amount of extraterrestrial global solar radiation.

In the air conditioning system of the present invention, the estimated amount of solar radiation during the morning shadow time is calculated by multiplying the extra-atmospheric global solar radiation during the shadow time by a preset coefficient, and the estimated amount of solar radiation during the afternoon shadow time is calculated by multiplying the extra-atmospheric global solar radiation during the shadow time by a coefficient calculated by dividing the actual measured amount of solar radiation before the shadow time by the extra-atmospheric global solar radiation.

In the air conditioning system of the present invention, solar radiation can be estimated under the conditions that it is a shadow time and the weather is determined to be sunny.

The air conditioning system of the present invention can be configured to estimate the near-floor temperature using the presence or absence of solar radiation at the target location as an additional parameter.

In the air conditioning system of the present invention, the presence or absence of solar radiation can be determined based on the shadow duration at the target location.

In the air conditioning system of the present invention, the shadow time at a target location can be determined based on a sky factor chart.

The air conditioning system of the present invention can be configured to estimate the near-floor temperature in the perimeter zone using a temperature estimation model generated by machine learning.

In the air conditioning system of the present invention, the temperature estimation model can be configured to estimate the near-floor temperature by further using some or all of the parameters selected from the following parameters as explanatory variables:
- Outside air temperature - Operating status of the air conditioner - Amount of heat emitted - Wind direction - Wind speed - Rainfall - Measurement value of the near-floor temperature sensor that measures the temperature near the floor in the perimeter zone - Operating status of the perimeter fan - Operating status of the variable air volume device facing the perimeter zone - Date - Time - Day of the week The air conditioning system of the present invention can be configured to input the amount of solar radiation and the presence or absence of solar radiation at the target location into the temperature estimation model as separate explanatory variables.

The air conditioning system of the present invention can provide the excellent effect of accurately estimating the air temperature near the floor in the perimeter zone.

1 is a schematic diagram showing an example of the configuration of an air conditioning system to which the present invention is applied. 1 is a schematic plan view showing an example of the arrangement of an air volume variable-volume device and a near-floor temperature sensor installed facing a perimeter zone in an air conditioning system to which the present invention is applied. FIG. 1 is a schematic diagram showing an example of the overall configuration of an air conditioning system of the present invention applied to a multi-story building. 1 is a schematic plan view showing an example of the arrangement of an air volume variable-volume device and a near-floor temperature sensor installed facing the perimeter zone of a reference floor in an air conditioning system to which the present invention is applied. FIG. FIG. 2 is a conceptual diagram illustrating an example of an input/output configuration of a temperature estimation model. 10 is a flowchart illustrating an example of a procedure for setting an operating condition of a variable air volume device or the like in an embodiment of the present invention. 1 is a conceptual diagram illustrating the temperature difference between the top and bottom of the room air and temperature control based on that difference. 10 is a graph illustrating an example of the relationship between a control deviation calculated based on an estimated value of the near-floor temperature and a temperature correction value. 10 is a graph illustrating an example of the relationship between the outside air temperature and the upper limit of the supply air temperature. 2 is a schematic diagram illustrating an example of a temperature distribution of indoor air near a perimeter zone of the air conditioning system of FIG. 1. FIG. 3 is a block diagram conceptually illustrating control using a temperature correction value in the present embodiment. 1 is a graph showing an example of fluctuations in the amount of solar radiation measured by a pyranometer (when no shielding object is present); 10 is a graph showing another example of fluctuations in the amount of solar radiation measured by a pyranometer (when a shield is present); 1 is a graph showing an example of the relationship between the amount of solar radiation measured by a pyranometer, the actual amount of solar radiation, and the estimated amount of solar radiation. 1 is a flowchart illustrating an example of a procedure for executing a method for estimating solar radiation of the present invention. 10 is a flowchart illustrating another example of the procedure for executing the method for estimating the amount of solar radiation of the present invention. 10 is a flowchart illustrating yet another example of the procedure for executing the method for estimating the amount of solar radiation of the present invention.

Embodiments of the present invention will be described below with reference to the accompanying drawings.

Figure 1 shows a schematic diagram of an example of an air conditioning system embodying the present invention. Conditioned air (supply air) A1 sent out from an air conditioner 1 is guided through an air supply duct 2 to a target space S, which is a room such as an office. Multiple air outlets 4 are installed in the ceiling 3 of the target space S, and the downstream side of the air supply duct 2 extending from each air conditioner 1 is connected to each air outlet 4. The conditioned air A1 circulating through the air supply duct 2 is supplied to the target space S from each air outlet 4. Although each air outlet 4 is shown as one, in reality each has multiple air outlets.

A variable air volume device 5 is provided in front of each air outlet 4 in the supply air duct 2. The variable air volume device 5 is a device also known as VAV (Variable Air Volume), and adjusts the volume of air passing through it based on the measured values by changing the opening of an internal damper and measuring the wind speed and volume either before or after the damper. When the supply air A1 is supplied to the target space S, the volume is adjusted by the variable air volume device 5.

The target space S can be divided into a perimeter zone P, such as near windows, which is easily affected by the exterior load, and an interior zone I, where the proportion of the total building heat load is relatively small. FIG. 1 shows one variable air volume device 5 facing the interior zone I and one facing the perimeter zone P. It also shows one air conditioner 1 (interior air conditioner 1a) that sends supply air A1 to the variable air volume device 5 facing the interior zone I, and one air conditioner 1 (perimeter air conditioner 1b) that sends supply air A1 to the variable air volume device 5 facing the perimeter zone P. (Note that this is a simplified schematic diagram; in an actual air conditioning system, the number and arrangement of variable air volume devices 5 and air conditioners 1, and the number of variable air volume devices 5 relative to air conditioner 1, are naturally determined appropriately depending on the size, shape, and thermal conditions of the target space S. In particular, while the air conditioner 1a for the perimeter zone P and the air conditioner 1b for the interior zone I are shown as separate units, it goes without saying that it is common for the two air conditioners 1a, 1b to be combined into a single unit with the supply air duct and return air duct branched, and this is of course included.)

Multiple intakes 7, 16 are provided on the ceiling 3 at positions corresponding to the interior zone I and the perimeter zone P, respectively. From each variable air volume device 5 facing the interior zone I or perimeter zone P, supply air A1 sent from the interior air conditioner 1a or perimeter air conditioner 1b, respectively, is supplied to the target space S through the outlet 4, where it mixes with the air (room air) A2 within the target space S.

A portion of the indoor air A2, mainly in the interior zone I, is taken in as return air A3 from the intake port 7 facing the interior zone I. A portion of the return air A3 is discharged outside the return air duct 8 as exhaust air A4, and a portion is mixed with fresh outside air A0 taken in through the return air duct 8 and returned to the interior air conditioner 1a, where the temperature and humidity are adjusted before being sent out again as supply air A1.

A portion of the indoor air A2, mainly in the perimeter zone P, is taken in as return air A3 from the intake port 16 facing the perimeter zone P, and the entire amount returns to the perimeter air conditioner 1b through the return air duct 17, where it is temperature-adjusted and sent out again as supply air A1. In this way, air circulates between the air conditioner 1 and the target space S. Note that while the interior zone I takes in outside air A0 to replace the air, the perimeter zone P is designed not to take in outside air A0 and to circulate all of the air. This is because it is expected that there will be fewer people in the perimeter zone P than in the interior zone I, and so less outside air is introduced to exhaust the carbon dioxide that increases with breathing than in the interior zone.

Such an air conditioning system responds to fluctuations in the air conditioning load within the target space S, for example, by using an air conditioning method known as a per-floor unit method (a single-duct variable air volume method completed on each floor). A temperature sensor 9 is installed at an appropriate location within the target space S (in this example, the ceiling 3) to measure the temperature of the indoor air A2. (Hereinafter, these temperature sensors 9 measuring the temperature of the indoor air A2 at or near the ceiling will be referred to as "ceiling temperature sensors." Also, below, the temperature of the indoor air A2 at the height where the ceiling temperature sensor 9 is installed will be referred to as the "ceiling temperature" for convenience, as needed.) Each variable air volume device 5 is equipped with a control device (VAV controller), which controls the variable air volume device 5 using PI control or similar to keep the deviation between the measurement value of the ceiling temperature sensor 9 and the temperature set value of the variable air volume device 5 close to zero, thereby supplying an appropriate volume of supply air A1 from the outlet 4. For example, during heating, if the measurement value of the ceiling temperature sensor 9 is lower than the set value of the indoor temperature (temperature of indoor air A2), the amount of supply air A1 is increased, and if the measurement value is close to the set value, the amount of supply air A1 is decreased.

The operating status of each device that makes up the air conditioning system is monitored and operated by the control device 10. The control device 10 is equipped with a controller that controls the operation of each air conditioner 1 (interior air conditioner 1a and perimeter air conditioner 1b), and acquires values such as the required air volume ratio of each variable air volume device 5 and the set temperature within the target space S, and based on these, determines the temperature and air volume of the supply air A1 supplied from each air conditioner 1.

The control device of each variable air volume device 5 acquires the ceiling temperature as a measurement value from each ceiling temperature sensor 9, calculates the required air volume based on the deviation from the set temperature, and adjusts the opening of the damper within the variable air volume device 5 based on the deviation from the air volume measured and calculated by an anemometer or other device provided in the variable air volume device 5, supplying an appropriate volume of supply air A1 from the outlet 4. That is, for example, when heating, if the measurement value of the ceiling temperature sensor 9 is lower than the target value, the supply volume of supply air A1 is increased, and if the measurement value is close to the target value, the supply volume is reduced.

Each air conditioner 1 sends out supply air A1 from its variable air volume device 5 to each variable air volume device 5, and the amount of supply air A1 sent out here is the sum of the required air volumes of one or more variable air volume devices 5 downstream. Therefore, the control device 10 uses the required air volume ratio, which is the ratio of the required air volumes of each variable air volume device 5, as an input signal and performs calculations based on each required air volume ratio to determine the supply air volume for each air conditioner 1.

In addition to the air volume control described above, actual air conditioning systems also perform control (load reset control) to adjust the supply air temperature (the temperature of supply air A1 sent out from air conditioner 1) according to the required air volume ratio. In variable air volume air conditioning systems, the required air volume ratio from each variable air volume device 5 increases as the load increases, and decreases as the load decreases. Therefore, not only the supply air volume of supply air A1 in air conditioner 1 but also the supply air temperature are appropriately changed according to the required air volume ratio from each variable air volume device 5. For example, during heating, if the required air volume is smaller than the rated air volume of the variable air volume device 5, the set value of the supply air temperature is lowered. Various methods can be used for load reset control. For example, a method can be used in which the set value of the supply air temperature is changed based on the opening information of the air volume variable devices 5 so that the opening of each air volume variable device 5 falls within a predetermined opening range, or a method can be used in which the deviation between the set temperature of the air volume variable devices 5 and the measured temperature (the measured temperature of the air at an appropriate location in the air conditioning system, for example the temperature of return air A3) is weighted and the set value of the supply air temperature is changed based on this weighting.

In this way, in an air conditioning system like the one shown in Figure 1, the supply air volume and intake air temperature are automatically changed to match the indoor load.

In this type of air conditioning system, the temperature of the indoor air A2 is measured by the ceiling temperature sensor 9, and so there may be a discrepancy between the temperature of the indoor air A2 that a person in the target space S is actually in contact with and the temperature of the indoor air A2 measured by the control device 10. In the example shown in Figure 1, the ceiling temperature sensor 9 is located at the height of the ceiling 3, but due to outdoor air conditions and the control conditions of each device, such as the variable air volume device 5 and the air conditioner 1, a difference will arise between the temperature of the indoor air A2 near the floor where the person is located and the temperature of the indoor air A2 around the ceiling temperature sensor 9 (near the ceiling).

As mentioned above, particularly in winter when the outside temperature is low, cold drafts can occur, causing discomfort to people in the perimeter zone P or nearby.

Therefore, in this embodiment, a perimeter fan 6 is installed near the floor of the perimeter zone P to correct the temperature distribution of the indoor air A2 in the perimeter zone P. The perimeter fan 6 is designed to blow the indoor air A2 in the target space S upward along the window surface. By operating this perimeter fan 6, a flow of indoor air A2 is created from near the floor of the perimeter zone P upward. This prevents the indoor air A2 near the window surface, cooled by outside air, from becoming a cold draft and moving toward the interior zone I inside the perimeter zone P, and instead allows it to be blown into the upper air inlet 16. Furthermore, by simultaneously blowing indoor air A2 toward the perimeter fan 6 from a nearby variable air volume device 5, combined with the operation of the perimeter fan 6, it is possible to supply high-temperature indoor air A2 to the vicinity of the outer edge of the perimeter zone P.

Furthermore, in this embodiment, as shown in Figures 1 and 2, optional near-floor temperature sensors 11 are provided near some of the air volume variable devices 5 located near the perimeter zone P in a plan view (Figure 1 shows the position of the near-floor temperature sensors 11 in a side view with a dashed line). The near-floor temperature sensors 11 are installed on or near the floor in the perimeter zone P, at a height where people and the like are present, and measure the temperature of the surrounding air. These near-floor temperature sensors 11 are used as parameters for a temperature estimation model that estimates the near-floor temperature, and the measured value is used as a parameter to be used for other near-floor locations rather than for the near-floor location in the target space in which they are installed.

Hereinafter, in this specification, the temperature of the indoor air A1 near the floor will be referred to as the "near-floor temperature." In this specification, "near-floor temperature" refers to the temperature of the air near the floor, between 0 cm and 10 cm, or between 10 cm and 170 cm, in the occupied area. Furthermore, "ceiling temperature" refers to the temperature of the air at a height of 170 cm or more above the floor and below the ceiling 3. In other words, the ceiling temperature sensor 9 may be installed at the height of the ceiling 3, or at a lower position (as long as it is at a height of 170 cm or more above the floor).

Here, the near-floor temperature sensor 11 that measures the near-floor temperature can be installed at a height of 0 cm or more but less than 10 cm when measuring the temperature of indoor air A1 on or near the floor, or at that height when measuring the temperature of indoor air A1 at a height of 10 cm or more but less than 170 cm (occupied area). Alternatively, in the latter case, the near-floor temperature sensor 11 can be installed at a height of 0 cm or more but less than 10 cm, and the temperature at the desired height can be calculated based on the measurement values from the ceiling temperature sensor 9 and the near-floor temperature sensor 11, with the difference being apportioned based on the distance between the two sensors.

The near-floor temperature sensor 11 is preferably installed inside the installation positions of each perimeter fan 6 (see Figure 1) in a plan view (i.e., the near-floor temperature sensor 11 is located further inside the perimeter fan 6 when viewed from the window or wall). Perimeter fans 6 are typically positioned to take in indoor air A2 from the side facing the interior of the target space S and send it upward. Therefore, if the near-floor temperature sensor 11 is installed inside the perimeter fan 6, the indoor air A2 near the floor surface taken in by the perimeter fan 6 moves and comes into contact with the near-floor temperature sensor 11, allowing the near-floor temperature sensor 11 to optimally obtain the near-floor temperature.

For ease of explanation, Figure 1 simply illustrates two air conditioners 1, one target space S, two air outlets 4, two variable air volume devices 5, one perimeter fan 6, and a total of two air inlets 7, 16. However, this is merely a schematic diagram for illustrative purposes; in an actual air conditioning system, the number of air conditioners 1 and variable air volume devices 5 installed may be changed, or supply air A1 may be directed to multiple target spaces S. The number of air outlets 4 installed per target space S may also be changed as appropriate depending on the size of the target space S, etc. In an actual air conditioning system, various other devices and sensors will be installed in addition to the devices shown here, but configuration not directly related to the spirit of the present invention has been omitted from the illustration.

Such an air conditioning system can be installed throughout a building, as shown in FIG. 3, for example. On each floor, as shown in FIG. 2, a near-floor temperature sensor 11 is installed near a portion of the variable air volume device 5 facing the perimeter zone P in a planar view. In the example shown here, one near-floor temperature sensor 11 is installed on each side of the perimeter zone P in a planar view (note that the "variable air volume device 5 facing the perimeter zone P" here roughly means "the variable air volume device 5 that supplies air to the perimeter zone P"). The temperature conditions in the perimeter zone P are easily affected by exterior loads (wind direction, sunlight, etc.) via windows and walls facing the outside air, and can vary significantly depending on the orientation of windows and walls even within the same building or target space S. However, it is believed that temperatures will be roughly similar within the same room and facing the same direction relative to the building.

In other words, the perimeter zones P of each target space S on each floor can, in principle, be divided into four main systems according to the temperature characteristics that differ depending on the direction. For example, a target space S with windows and walls facing northeast and windows and walls facing northwest can be considered to have two perimeter zones: a northeast perimeter zone P and a northwest perimeter zone P. (Note, however, that this is not necessarily the case for floors or target spaces S with special shapes, and it is also possible to imagine cases where they can be considered to have more perimeter zones P.) A temperature estimation model M, described below, is then generated for each perimeter zone P corresponding to each of these systems.

Note that the division of perimeter zones by direction as described above and the creation of a temperature estimation model based on this are merely examples. When actually generating a temperature estimation model, the way in which perimeter zones are divided and the way in which a temperature estimation model is created can be set appropriately depending on the temperature conditions at the site, etc.

Furthermore, while Figure 2 only illustrates the variable air volume device 5 and near-floor temperature sensor 11 installed in the perimeter zone P, in reality, variable air volume devices 5 are also appropriately placed in the interior zone I, and of course, devices such as the perimeter fan 6 and ceiling temperature sensor 9 are also placed as shown in Figure 1. Furthermore, Figure 3 illustrates a simplified building and air conditioning system as an overall view of the air conditioning system, and one air conditioner 1 (perimeter air conditioner 1b) and one variable air volume device 5 are shown on each floor, but it goes without saying that the number of floors in an actual building may differ from those shown here, and that more air conditioners 1 and variable air volume devices 5 may be installed on each floor. Of course, the floor plan of each floor and the placement of the variable air volume devices 5 in each target space S may differ from the configuration shown in Figure 2.

On some of the floors that make up the building, as shown in Figure 4, near-floor temperature sensors 11 are installed near all variable air volume devices 5 facing the perimeter zone P. This is to collect actual near-floor temperature measurements that will be used as learning data D (see Figure 3) for generating the temperature estimation model M, which will be described later.

As shown in Figure 4, the floor (hereinafter referred to as the "reference floor") on which near-floor temperature sensors 11 are installed for all variable air volume devices 5 facing the perimeter zone P may be, for example, a specific floor among multiple floors with a common floor plan. This is because it is expected that temperature conditions will be generally similar if the floor plan is common. In other words, for example, if the 11th to 40th floors of a building have the same floor plan, and the target floors are the 11th to 40th floors, the 25th floor can be set as the reference floor, and the actual near-floor temperature measurements collected on the 25th floor can be used as learning data D to generate a temperature estimation model M, which can then be used to estimate near-floor temperatures on the other floors from the 11th to 40th floors. Even if the floor plan is common, significant differences in height can lead to corresponding differences in temperature conditions. In such cases, for example, the 18th and 32nd floors may be set as different reference floors. That is, a first temperature estimation model is generated based on learning data collected using the 18th floor as the first reference floor, and is used to estimate near-floor temperatures on the other floors from the 11th to the 24th. A second temperature estimation model is generated based on learning data collected using the 32nd floor as the second reference floor, and is used to estimate near-floor temperatures on the other floors from the 25th to the 40th. Alternatively, if the temperature situation changes significantly across a specific floor due to factors such as the shadow of a nearby building, different temperature estimation models may be applied above and below that specific floor. In this way, the setting of the reference floor and the target to which a certain temperature estimation model is applied can be determined appropriately taking into account various conditions.

As shown in Figure 3, the air conditioning on each floor is operated by an air conditioner 1, a variable air volume device 5, a perimeter fan 6, a ceiling temperature sensor 9, a near-floor temperature sensor 11, and a control device 10. The operating status of the air conditioning on each floor is monitored and controlled by a central monitoring device 12 connected to the control device 10 on each floor. The central monitoring device 12 is a device that comprehensively monitors the air conditioning system and other systems (electrical systems, etc.) for the entire building. A temperature estimation unit 13 is further connected to the central monitoring device 12, and a model generation unit 14 is connected to the temperature estimation unit 13. The temperature estimation unit 13 and the model generation unit 14 are information processing devices such as personal computers. As described below, the temperature estimation unit 13 estimates the near-floor temperature in the perimeter zone P (see Figures 1 and 2) on each floor. The model generation unit 14 generates a temperature estimation model M that the temperature estimation unit 13 uses to estimate the near-floor temperature. The temperature estimation model M is generated based on the learning data D and is a model that estimates the near-floor temperature at a certain location using parameters related to the near-floor temperature as explanatory variables. That is, for example, the near-floor temperature at multiple locations in a perimeter zone P on the north side of a specific target space S on a certain floor is estimated based on various parameters described below.

In addition, the air conditioning system of this embodiment uses solar radiation as one of the parameters for estimating the near-floor temperature (more specifically, as one of the explanatory variables input into the temperature estimation model M to estimate the near-floor temperature), and the measured value of solar radiation is input to the central monitoring device 12 from a pyranometer 100 installed on the roof of the building (see Figure 3).

The air conditioning system of this embodiment also includes a solar radiation estimator 101 for estimating solar radiation. The solar radiation estimator 101 calculates an estimated value of solar radiation at a target time under specific conditions based on the measured solar radiation values input to the central monitoring device 12, the extra-atmospheric global solar radiation, and the like. The model generator 14 and the temperature estimation model M use the estimated value calculated by the solar radiation estimator 101 in place of the actual measured value from the pyranometer 100 as needed to generate the temperature estimation model M and estimate the near-floor temperature at each location. While the solar radiation estimator 101 is shown here as part of the temperature estimator 13, it may be, for example, an external device connected to the central monitoring device 12, or the solar radiation estimator 101 may be configured as part of the central monitoring device 12. The estimation of solar radiation by the solar radiation estimator 101 will be described in detail below.

The temperature estimation model M can use the following parameters, for example, to estimate the near-floor temperature.
Outside air temperature (values obtained at multiple heights (e.g., rooftop, high-rise, and low-rise) may be used)
Amount of solar radiation (actual measured value of solar radiation measured by the pyranometer 100, or estimated value of solar radiation calculated by the solar radiation amount estimation unit 101)
- Presence or absence of sunlight (is the target location in the sun or shade? This parameter will be explained in detail later)
The operating state of the air conditioner 1 (off/cooling/heating, target value of supply air temperature, supply air volume. The value of the air conditioner 1 responsible for air conditioning at the position where the near-floor temperature is to be estimated may be used, or the value of another air conditioner 1 may be used.)
Actual measurement value of supply air temperature in air conditioner 1. Amount of outflow heat (amount of heat flowing from the target space S to the outside. Can be calculated based on the temperature difference between the inside and outside of the room).
The operating state of the variable air volume device 5 facing the perimeter zone P (on/off, set temperature, required air volume)
- Measurement value of ceiling temperature sensor 9 (current value, and also values obtained earlier)
Wind direction (values obtained at multiple heights (e.g., rooftop, high-rise, and low-rise) may be used)
Wind speed (values obtained at multiple heights (e.g., rooftop, high-rise, and low-rise) may be used)
Rainfall amount Measurement value of the near-floor temperature sensor 11 (measurement value of the near-floor temperature sensor 11 installed at a position other than the position where the near-floor temperature is to be estimated)
- Perimeter fan 6 operating status (on/off, air volume)
- Date (at least one of month and day)
- Time (at least one of hour and minute)
・Day of the week (Sun, Mon, Tues, Wed, Thurs, Fri, Sat, or whether it's a holiday or a weekday)

An example of the input/output configuration of the temperature estimation model M is shown in Figure 5. The input explanatory variables are the outside air temperature (average value for the past 30 minutes) obtained from the central monitoring device 12, the amount of solar radiation (average value for the past 30 minutes), the presence or absence of solar radiation, the operating status of each air conditioner 1 in the perimeter zone P and interior zone I, the actual measured values of the supply air temperature therein, the operating status of the variable air volume device 5 facing the perimeter zone P, the amount of heat emitted, and the measurements of the ceiling temperature sensors 9 installed in various locations. These are used as input values, and an estimated value of the near-floor temperature at each target location is output.

Such a temperature estimation model M is created for each variable air volume device 5 system unit, so that the near-floor temperature at the corresponding position can be output for each perimeter air conditioner 1b. In the example shown here, a temperature estimation model M is assumed to be used for an air conditioning system with a total of eight variable air volume devices 5, and is configured to output eight estimated near-floor temperatures corresponding to the positions of the eight variable air volume devices 5. Note that for positions where near-floor temperature sensors 11 are installed, the measured values can be used, so there is no need to estimate the near-floor temperature using the temperature estimation model.

Here, as will be described later, in the air conditioning system of this embodiment, which controls the variable air volume device 5 based on estimated values of near-floor temperatures, research by the present inventors has revealed that by using the measured values of the ceiling temperature sensors 9 installed in various locations as explanatory variables input into the temperature estimation model M, it is possible to estimate the near-floor temperature at a target location with particularly high accuracy.

The air conditioning system of this embodiment is intended to estimate the near-floor temperature and control it to approach a target temperature (target near-floor temperature). However, the direct target of control is the air temperature near the ceiling (ceiling temperature). By manipulating the set value of this ceiling temperature (target ceiling temperature), the system indirectly maintains the near-floor temperature at a desirable level. As will be explained in more detail later, this type of control first sets the target ceiling temperature based on the estimated near-floor temperature at a given point in time. The air volume of the variable air volume device 5 is adjusted accordingly, and the supply air is directed toward the perimeter fan 6 and supplied to the perimeter zone P, thereby adjusting the near-floor temperature in the perimeter zone P. This cycle gradually brings the near-floor temperature closer to the target value. In other words, the temperature control performed near the ceiling is reflected in the near-floor temperature with a certain time lag. Because the near-floor temperature is thus affected by the ceiling temperature through the operation of the air conditioning system itself, the measurement value of the ceiling temperature sensor 9 is an effective explanatory variable for estimating the near-floor temperature. Furthermore, when estimating the current near-floor temperature, using the measurement value from the ceiling temperature sensor 9 at some point in the past is particularly effective in accurately estimating the near-floor temperature, as that value (the ceiling temperature at that time) has a significant impact on the current near-floor temperature.

In the example shown in Figure 5, current and past values from eight ceiling temperature sensors 9 are input as explanatory variables into the temperature estimation model M, and estimates of near-floor temperatures at eight locations are calculated. Past values are calculated in five-minute increments, taking values up to 30 minutes ago. Note that the number of ceiling temperature sensor measurements input into the temperature estimation model (eight in Figure 5) and the number of target estimates (near-floor temperatures) output (also eight in Figure 5) are merely examples, and may be changed as appropriate depending on the number of ceiling temperature sensors installed in the actual system and the number of locations where near-floor temperatures are desired to be estimated. Furthermore, the number of past values to input, how many minutes back past values to use, and the interval between past values to use may be set as appropriate when designing the air conditioning system or model.

As mentioned above, when estimating the near-floor temperature at a certain location, it is possible to use the actual measurement value of a near-floor temperature sensor 11 installed at a different location. For example, when attempting to estimate the near-floor temperature at a location on the right side of Figure 2, it is possible to use the measurement value of a near-floor temperature sensor 11 installed on the same side of the same room (within the same system's perimeter zone P) as an explanatory variable. However, in the target space S, such as a room in an office building, partitions are often installed on the floor or equipment that emits heat is located, so even in the same perimeter zone within the same room, the thermal conditions often differ significantly depending on the location. As a result, if the actual measurement value of a near-floor temperature sensor 11 at a different location is input as an explanatory variable, the correlation between the near-floor temperature at the target location and the actual measurement value at that different location is low, and the output estimated value may deviate from the actual value. On the other hand, the thermal conditions near the ceiling are less likely to change or vary from location to location due to factors such as heat exhaust from partitions or equipment compared to those near the floor. Therefore, although it depends on the actual conditions within the target space S, measurements from ceiling temperature sensors 9 installed on or near the ceiling 3 are likely to be more useful as explanatory variables. For example, by using a model that takes into account the trends and patterns of ceiling temperature, such as the temperature estimation model M shown in Figure 5, it is possible to accurately estimate near-floor temperatures. However, for example, if you want to estimate near-floor temperatures at a certain location and it is known that the thermal conditions at that location are similar to those at another location where a near-floor temperature sensor 11 is installed, it may be effective to use the measurements from the near-floor temperature sensor 11 at that other location as explanatory variables. Furthermore, while using current and past values from the ceiling temperature sensor 9 as explanatory variables, measurements from near-floor temperature sensors 11 at other locations may also be used to further improve accuracy.

The inventors have also found that the near-floor temperature can be estimated more accurately by further using values related to solar radiation (measured or estimated solar radiation, or the presence or absence of solar radiation at the target location) as explanatory variables, as described below. Here, when considering both the amount of solar radiation and the presence or absence of solar radiation, for example, the value obtained by multiplying the measured or estimated amount of solar radiation by the presence or absence of solar radiation (zero or one) at the target location can be used as an explanatory variable. Alternatively, the presence or absence of solar radiation can be prepared as a data table separate from the amount of solar radiation, and these can be used as separate explanatory variables. In particular, in the latter case, by inputting the quantitative variable of solar radiation and the qualitative variable of the presence or absence of solar radiation separately as separate explanatory variables into the temperature estimation model M, complex simulations can be eliminated, reducing the effort required for creating the model and preparing the data, while enabling the construction of a highly accurate temperature estimation model M. Alternatively, only either the measured or estimated amount of solar radiation or the presence or absence of solar radiation at the target location can be used as an explanatory variable related to solar radiation.

Of the above parameters, the outdoor temperature, rainfall, and solar radiation may also affect the predicted value of the near-floor temperature with a time lag. Therefore, for these values, the actual measured values prior to the time point desired for prediction (for example, approximately 10 to 30 minutes prior) may also be used as parameters.

The operating state of the perimeter fan 6 can also be reflected in the temperature estimation model M. In this case, taking into consideration that the thermal situation in the perimeter zone P is particularly significantly affected by the operating state of the perimeter fan 6, instead of or in addition to using the operating state of the perimeter fan 6 as an explanatory variable, different temperature estimation models M themselves may be used depending on the operating state of the perimeter fan 6. That is, for example, a temperature estimation model M when the perimeter fan 6 is on (temperature estimation model M _ON ) and a temperature estimation model M when the perimeter fan 6 is off (temperature estimation model M _OFF ) are generated, and the temperature estimation models M _ON and M _OFF are switched depending on the operating state of the perimeter fan 6.

In addition, instead of the parameters exemplified here, equivalent parameters with different units or definitions, or related parameters, can be used. Furthermore, in addition to the parameters exemplified above, some other parameter related to the near-floor temperature may also be used. Naturally, processing such as normalization or centering of values may also be performed as appropriate.

The learning data D is a set of data related to air conditioning operation accumulated in the central monitoring device 12 over an appropriate period (e.g., from a few days to a year) from the control devices 10 on each floor (particularly the reference floor) and other sensors (not shown), and is stored in the temperature estimation unit 13. The model generation unit 14 generates a temperature estimation model M based on the learning data D and stores it in the temperature estimation unit 13. The temperature estimation unit 13 also includes a correction value calculation unit 15. The correction value calculation unit 15 calculates a temperature correction value based on the near-floor temperatures at each location in the perimeter zone P on each floor calculated using the temperature estimation model M. The temperature correction value is a correction value added to the set temperature for each variable air volume device 5 facing the perimeter zone P. The role of this temperature correction value will be explained again later.

The generation of the temperature estimation model M by the model generation unit 14 will now be described. Various types of models can be used as the temperature estimation model M, such as linear regression, ridge regression, gradient boosting, and random forest. However, if the temperature estimation model M is generated using a multi-input/multi-output neural network, for example, the near-floor temperature can be estimated with high accuracy. In particular, the use of a two-layer linear neural network makes it possible to achieve high-accuracy estimations without overlearning, even in a short learning period of around two weeks. High-accuracy estimations are also possible using a three-layer neural network or a time-series neural network such as LSTM.

The learning data D used to generate the temperature estimation model M is a data set that records various parameters (such as the parameters exemplified above) related to near-floor temperatures at various times or locations when an air conditioning system such as those shown in Figures 1 to 3 is actually operated, as well as actual measured values of near-floor temperatures. The actual measured values of near-floor temperatures are obtained from near-floor temperature sensors 11 installed in various locations in the perimeter zone P on the reference floor. In addition, some of the parameters related to near-floor temperatures can be obtained from the air conditioner 1, ceiling temperature sensor 9, control unit of the variable air volume device 5, etc.

Learning data D records the above parameters at multiple points in time, as well as the actual measured values of the near-floor temperature at each point in time obtained on the reference floor.

The collection of the learning data D may be performed, for example, on the condition that all of the following conditions are met:
Whether heating operation is being performed. In the perimeter zone P, the cold air problem described above occurs only when the outdoor temperature is low and heating operation is being performed. In other words, it is only during heating operation that it is necessary to estimate the near-floor temperature, calculate a temperature correction value based on that, and perform operation using that temperature correction value. Therefore, only parameters recorded during heating operation are used as learning data D.
Whether the outdoor temperature is lower than a preset threshold. The above problem occurs only when the outdoor temperature is lower than the indoor air temperature by a certain amount, so estimation of the near-floor temperature and calculation of the temperature correction value are only necessary when the outdoor temperature is low. Therefore, learning data D is collected only when the outdoor temperature is lower than the threshold.
Whether the variable air volume device 5 is in operation. If the variable air volume device 5 itself is off, it will not operate using the temperature correction value, so there is no need to estimate the temperature near the floor or calculate the temperature correction value. Therefore, if the variable air volume device 5 is not in operation, the learning data D is not collected.

However, this does not apply if the amount of acquired learning data is small; learning data D may be collected as appropriate even when at least some of the above conditions are not met.

The model generation unit 14 performs machine learning using the training data D to generate a temperature estimation model M that estimates the near-floor temperature at each location in the perimeter zone P on each floor based on various parameters. When generating the temperature estimation model M using a neural network, the model generation unit 14 appropriately selects and discards multiple parameters from the various parameters described above recorded in the training data D, and also performs processing and weighting such as normalization and centering on each parameter as necessary to form a pattern of options. The near-floor temperature estimated from the formed pattern is compared with the actual near-floor temperature acquired by the near-floor temperature sensor 11 on the reference floor, and the pattern is repeatedly modified, until a highly accurate temperature estimation model M is generated through machine learning. The generated temperature estimation model M is a model that estimates the near-floor temperature at a certain location from parameters related to the near-floor temperature in the target air conditioning system.

In the air conditioning system described above, an example of the procedure for operating the variable air volume device 5 facing the perimeter zone P using the estimated near-floor temperature and a temperature correction value calculated based on this will be explained with reference to the flowchart in Figure 6.

The temperature estimation unit 13 (see FIG. 3) acquires information about the operating status of each part of the air conditioning system via the central monitoring device 12 (step S1). The acquired information includes, for example, the supply air temperature (temperature of supply air A1) for each air conditioner 1, the actual temperature of the indoor air A2 acquired by the ceiling temperature sensor 9 installed in the target space S on each floor, the set temperature for each target space S and variable air volume device 5, the blowing air volume (damper opening) of each variable air volume device 5, the outdoor air temperature measured by a temperature sensor (not shown), the date and time, the amount of solar radiation measured by the pyranometer 100, and the presence or absence of solar radiation at each location. The near-floor temperature at each location in the perimeter zone P of each target space S estimated using the temperature estimation model M is also acquired in step S1.

Next, based on the acquired information, it is determined whether or not to operate each air conditioner 1 constituting the air conditioning system using the temperature correction value at the variable air volume device 5 located downstream of it (step S2). In this step S2, whether or not to operate using the temperature correction value is determined based on, for example, whether or not all of the following conditions are met (note that the conditions listed below are merely examples, and other conditions may also be used):
Heating operation is in progress. In the perimeter zone P, the above-described cold air problems occur only when the outside air temperature is low and heating operation is in progress, so operation using the temperature correction value is performed only when heating operation is in progress.
The outdoor air temperature is lower than a preset threshold. The above-mentioned problem occurs only when the outdoor air temperature is relatively low compared to the indoor air temperature, so operation using the temperature correction value is performed only when the outdoor air temperature is below the threshold. Note that the threshold used here is preferably set to, for example, two stages, and the higher threshold is used when changing the temperature correction value from enabled to disabled, and the lower threshold is used when changing it from disabled to enabled, as this prevents excessively frequent switching.

Once it has been determined whether or not to use a temperature correction value for each air conditioner 1, the system then sets, for each individual air conditioner 1, whether or not to use a temperature correction value for each downstream variable air volume device 5, and the specific numerical value. First, for one air conditioner 1, it is determined in step S3 whether or not it was determined in the previous step S2 that the downstream variable air volume device 5 will operate using a temperature correction value.

If none of the variable air volume devices 5 downstream of the target air conditioner 1 are to operate using a temperature correction value, each variable air volume device 5 downstream of that air conditioner 1 is set to not use a temperature correction value. In step S4, one variable air volume device 5 is set to not use a temperature correction value (temperature correction value = 0). In the next step S5, it is determined whether any variable air volume devices 5 downstream of the target air conditioner 1 have not yet had a temperature correction value set. If there is an unset variable air volume device 5, the process moves on to another variable air volume device 5 (step S6) and sets the temperature correction value = 0 for that variable air volume device 5 (step S4). This process is repeated until temperature correction values have been set for all variable air volume devices 5 downstream of the target air conditioner 1, and the process moves on to step S7.

In step S7, the upper limit of the supply air temperature is set for the target air conditioner 1. The upper limit of the supply air temperature for the air conditioner 1 is set by the central monitoring device 12, and normally the value set in the central monitoring device 12 can be used as is. However, in this embodiment, this upper limit setting is changed only in specific cases. How the upper limit of the supply air temperature is changed will be explained later when explaining step S16, but in step S7, the value set in the central monitoring device 12 is used. Once step S7 is complete, proceed to step S17, which will be described later.

If it is determined in step S3 that it has been decided that at least some of the variable air volume devices 5 downstream of the target air conditioner 1 will operate using the temperature correction value, the process proceeds to step S8. In step S8, it is individually determined whether or not to enable the temperature correction value for each variable air volume device 5 located downstream of the target air conditioner 1. The determination in step S3 is made, for example, based on whether or not all of the following conditions are met:
- There is no malfunction or other problem with the variable air volume device 5 in question.
- The operation of the corresponding variable air volume device 5 is turned on.

Once it has been determined whether the temperature correction value is enabled or disabled for each variable air volume device 5, proceed to step S9. In step S9, it is determined whether operation using the temperature correction value for a particular variable air volume device 5 was enabled in the previous step S8. If the temperature correction value for the relevant variable air volume device 5 was set to disabled, proceed to step S10, where the temperature correction value for the relevant variable air volume device 5 is set to 0. If it is determined in step S9 that the temperature correction value for the relevant variable air volume device 5 was set to enabled, proceed to step S11, where the temperature correction value is calculated.

Below, we will explain how to calculate this temperature correction value, but first, we will explain the significance of the temperature correction value. As mentioned above, the temperature correction value is a correction value added to the set temperature for each variable air volume device 5 facing the perimeter zone P. This correction value is set to correct the discrepancy between the measured temperature and the perceived temperature, taking into account the fact that the ceiling temperature sensor 9 is installed at a height above the floor. In the air conditioning system shown in Figure 1, the actual measurement value of the indoor air A2 is recognized as the measurement value of the ceiling temperature sensor 9 installed at the height of the ceiling 3. However, since people are located near the floor in the target space S, as explained above, a discrepancy is likely to occur between the perceived temperature of people and the temperature recognized as the measurement value of the ceiling temperature sensor 9, especially in the perimeter zone P.

During heating, if the measurement value (called ceiling temperature _T1 ) of ceiling temperature sensor 9 is lower than the set value (called room temperature set value _TSP ), the air conditioning system, which is comprised of control device 10, air conditioner 1, and variable air volume device 5, operates each device so that ceiling temperature _T1 approaches room temperature set value _TSP . However, the actual temperature of room air A2 near the floor is often lower than ceiling temperature _T1 . For this reason, even if ceiling temperature _T1 approaches room temperature set value _TSP sufficiently, there may be a difference between the temperature near the floor (called _T2 ) and ceiling temperature _T1 , resulting in a situation where people near the floor do not receive sufficient warmth. To correct this, in this embodiment, an estimated value of the near-floor temperature _T2 (referred to as near-floor temperature estimated value _T2E ) is first calculated (step S1), a target value for the near-floor temperature _T2 (referred to as near-floor temperature target value _T2SP ) is set, a temperature correction value (referred to as β) corresponding to the deviation between these values (referred to as control deviation _E2 ) is calculated, and a set temperature value (referred to as ceiling temperature target value _T1SP ) obtained by adding this value is set for each variable air volume device 5 (step S11). The ceiling temperature target value _T1SP is calculated by adding the temperature correction value β to the original set value for the temperature of the indoor air A2 (indoor temperature set value _TSP ), and each device is operated using this as the target value, thereby correcting the temperature situation near the floor.

Management of the near-floor temperature using this temperature correction value β can also be explained using a diagram such as that shown in Figure 7. In Figure 7, the left vertical line represents the near-floor temperature _T2 , and the right vertical line represents the ceiling temperature _T1 . For example, if the indoor temperature setpoint _TSP is 23°C and the air conditioning system is operated based on this, resulting in a ceiling temperature _T1 of 23°C, the near-floor temperature _T2 will be, for example, 20°C due to the temperature difference between the upper and lower areas near the ceiling and floor. Since the estimated near-floor temperature _T2 (estimated near-floor temperature _T2E ) is lower than the near-floor temperature target value _T2SP set in accordance with the indoor temperature setpoint _TSP (or the reference value _T0 , which will be described next), a temperature correction value β is calculated in accordance with this difference (corresponding to the control deviation _E2 ), and a setpoint (ceiling temperature target value _T1SP ) incorporating this into the indoor temperature setpoint _TSP is set on the ceiling side, which is set to 26°C. As a result, the temperature difference near the floor surface is compensated for, resulting in a comfortable temperature (23°C).

However, the temperature difference is not uniform and may vary depending on the conditions. For example, if sunlight enters the perimeter zone P and warms the floor, or if the outdoor temperature rises and the intrusion of cold air decreases, the temperature difference will decrease. In this case, if the temperature correction value β is set to a uniform value (e.g., 3°C) without using the deviation between the estimated near-floor temperature _T2E and the indoor temperature setpoint _TSP as described above, the near-floor temperature may end up rising too much. Conversely, if the temperature difference is large, the near-floor temperature may be insufficient even if the temperature correction value β is added to the setpoint. Therefore, as described above, by calculating the temperature correction value β each time based on the deviation between the estimated near-floor temperature _T2E and the indoor temperature setpoint _TSP , the temperature conditions near the floor can be maintained optimally. In other words, rather than setting a uniform temperature correction value for the setpoint, the temperature correction value can be adjusted according to the temperature conditions near the floor.

An example of the procedure for calculating the temperature correction value β by the correction value calculation unit 15 will be described below. First, a control deviation (referred to as _E2 ) that is the basis for the temperature correction value β is calculated by the following formula.
[Equation 1]
Control deviation E ₂ = target value of near-floor temperature T _2SP - estimated value of near-floor temperature T _2E (1)
Near-floor temperature target value T _2SP = reference value T ₀ +k・α−γ ...(2)

The control deviation _E2 is the deviation between the near-floor temperature target value _T2SP and the near-floor temperature estimated value _T2E , and the air conditioning system is operated in a direction to eliminate this deviation.

The near-floor temperature target value _T2SP is a value set as the target value for the near-floor temperature _T2 , which is the control target of this system, and is calculated based on the reference value _T0 and other values, as shown in equation (2). The reference value _T0 is the set temperature value for the variable air volume device 5 determined by the central monitoring device 12, and is, for example, 23°C. α is a correction value added to the set temperature for the variable air volume device 5 based on operation by a person in the target space S, and can take two values, for example, 0 and 1 (the initial value is 1). k is a coefficient that determines the weighting of α when calculating the control deviation _E2 . γ is a value set as an adjustment parameter in the air conditioning system, taking into account the difference in air temperature between the height close to the floor (the height of the area where the air temperature is estimated and desired to be operated) and the occupied area. The indoor temperature set value _TSP is calculated by adding a correction value α from the indoor side to a centrally determined reference value _T0 , for example, as reference value _T0 + k α, and an adjustment parameter γ is further added to this to set the near-floor temperature target value _T2SP as shown in the above equation (2). The adjustment parameter γ is set to a value such as 0.5°C or 1°C, taking into account the actual air conditions in the perimeter zone P.

After the control deviation _E2 is calculated, the effective control deviation _E2eff is calculated, for example, from the relationship shown in the following formula.
[Equation 2]
E _2eff = E ₂ + δ (E ₂ ≦-δ)
E _2eff =0 (-δ<E ₂ <δ)
E _2eff = E ₂ - δ (δ≦E ₂ )

δ is a value set as a temperature dead band. That is, _E2eff = 0 is set in the range of -δ< _E2 <δ, with _E2 = 0 as the center, and in other ranges, i.e., in the range where the absolute value of the control deviation _E2 is equal to or greater than the threshold value (δ), the effective control deviation _E2eff is calculated linearly in accordance with the increase or decrease in the control deviation _E2 . This is effective for preventing frequent switching of the operating state in the range where the absolute value of the control deviation _E2 is small.

Using the effective control deviation _E2eff thus determined as a variable, the temperature correction value β is determined, for example, according to the relationship shown in Figure 8. In the example shown here, PI control is used, and when the effective control deviation _E2eff is -0.5°C or greater, the temperature correction value β is determined to be proportional to the effective control deviation _E2eff (in the example shown, the proportionality coefficient is 1.25). When the estimated near-floor temperature _T2E is lower than the near-floor temperature target value T2SP determined in accordance with the reference value _T0 in the central monitoring _{device 12} , the temperature correction value β is added to the set temperature in the variable air volume device 5 at the corresponding position to raise the set temperature (ceiling temperature target value _T1SP ) and increase the blowout volume.

In the example shown here, the upper limit of the temperature correction value β is set to +2° C., and if the effective control deviation _E2eff is 2° C. or more, the temperature correction value is uniformly +2° C. Note that the calculation formula for the control deviation _E2 or the effective control deviation _E2eff shown in Equation 1 above and the relationship between the effective control deviation _E2eff and the temperature correction value β shown in Figure 8 are merely examples, and the control deviation _E2 , the effective control deviation _E2eff , and the temperature correction value β may be determined by taking into account other variables or by using relationships different from those shown in Equation 1 and the example of Figure 8.

The target near-floor temperature value T _2SP and the target ceiling temperature value T _1SP calculated by the above procedure are expressed by the following formulas, for example: The following formula 3 applies when k=1 and γ=1.
[Equation 3]
T _2SP = T ₀ + α-1 ... (3)
T _1SP = T ₀ + α + β ... (4)

Once the temperature correction value has been set for the target variable air volume device 5 (steps S10 and S11), proceed to step S12. In step S12, it is determined whether there is a variable air volume device 5 downstream of the currently targeted air conditioner 1 for which a temperature correction value has not yet been set. If there is an unset variable air volume device 5, the target is switched to another variable air volume device 5 (step S13), and a temperature correction value is set for that variable air volume device 5 (steps S9 to S11).

Once the temperature correction values have been set for all variable air volume devices 5 downstream of the target air conditioner 1, proceed to step S14. In step S14, a new determination is made as to whether any of the variable air volume devices 5 downstream of the target air conditioner 1 are currently operating with the temperature correction value enabled. If the determination here is NO, proceed to step S15. In step S15, the upper limit value of the supply air temperature for the target air conditioner 1 is set to the same value as the setting value in the central monitoring device 12.

If the determination in step S14 is YES, i.e., if there is a variable air volume device 5 downstream of the target air conditioner 1 that is operating with the temperature correction value enabled, proceed to step S16. In step S16, an upper limit value for the supply air temperature is set for the target air conditioner 1, separate from the set value in the central monitoring device 12.

As mentioned above, the problem of cold drafts in the perimeter zone P occurs when the outside air temperature is low. For this reason, in this embodiment, operation is performed using a temperature correction value, with the outside air temperature being lower than a threshold as one of the conditions. Meanwhile, in air conditioning systems such as those shown in Figures 1 to 3, the amount of heat supplied to the target space S is determined by the blown air volume of each variable air volume device 5 and the supply air temperature from the air conditioner 1. In other words, if the supply air temperature of the air conditioner 1 is high, the blown air volume required to supply the same amount of heat will be correspondingly smaller. If the blown air volume from the variable air volume device 5 facing the perimeter zone P is reduced, the air temperature difference between areas of the perimeter zone P will increase, increasing the likelihood of a cold draft occurring.

Therefore, when operating using a temperature correction value, in step S16, the upper limit of the supply air temperature is set based on the outside air temperature, for example, using the relationship shown in Figure 9. In this example, the upper limit of the supply air temperature is lowered when the outside air temperature is below 10°C. When the outside air temperature is between 10°C and 0°C, the supply air temperature is lowered linearly with changes in the outside air temperature (in Figure 9, the proportionality coefficient is 0.6). When the outside air temperature is below 0°C, the upper limit of the supply air temperature is set uniformly to 25°C. This prevents the supply air temperature from rising too much when the outside air temperature is low, ensuring the volume of air blown from the variable air volume device 5 facing the perimeter zone P. Note that since the determination that the outside air temperature is below the threshold was made in the previous step S2, it is sufficient to specifically set the upper limit of the supply air temperature in step S16.

Once the upper limit of the supply air temperature has been set (step S7, S15, or S16), proceed to step S17. In step S17, it is determined whether any air conditioners 1 remain that have not yet been configured for operation using the temperature correction value for their downstream variable air volume devices 5. If so, the target is shifted to another air conditioner 1 (step S18), and the downstream variable air volume devices 5 are configured for operation using the temperature correction value and an upper limit of the supply air temperature is set (steps S3 to S16). Once the downstream variable air volume devices 5 and upper limit of the supply air temperature have been configured for all air conditioners 1, proceed to step S19. In step S19, the settings related to the operating conditions determined in steps S2 to S18 are input into the central monitoring unit 12. The central monitoring unit 12 operates each component of the air conditioning system based on the recorded settings.

In this way, the air conditioning system of this embodiment estimates the near-floor temperature at each location in the perimeter zone P. If the estimated near-floor temperature for a certain location is low, a temperature correction value is added to the set temperature of the variable air volume device 5 facing that location (step S11), and the set temperature is raised for operation. The variable air volume device 5 appropriately adjusts the blown air volume according to the raised set temperature, increasing the amount of air sent from the outlet 4 to the perimeter zone P. Furthermore, if the outside air temperature is low, the upper limit of the supply air temperature from the air conditioner 1 is lowered (step S16), ensuring the blown air volume from the variable air volume device 5. As a result, the indoor air A2 (see Figure 1) supplied from the variable air volume device 5 reaches the vicinity of the floor and the perimeter fan 6. In this way, the temperature distribution near the floor is corrected based on the estimated near-floor temperature, as shown in Figure 10. That is, warm air (room air A2) reaching the perimeter fan 6 from the variable air volume device 5 is sent to the upper intake port 16 and taken in as exhaust air A4, creating an upward flow of warm air, which blocks the cold air that forms at the outermost perimeter of the perimeter zone P and prevents cold drafts from entering the interior. In the perimeter zone P and the interior zone I further inside, warm air fills up to the floor level, achieving optimal air conditioning.

Such control is possible, for example, by installing near-floor temperature sensors 11 at locations corresponding to all variable air volume devices 5 located near the perimeter zone P for the target space S on all floors of a building, as shown in Figure 4, and measuring the near-floor temperature at each location. However, if near-floor temperature sensors 11 were installed in the entire perimeter zone P on each floor of a building, the number of near-floor temperature sensors 11 would be enormous, increasing installation costs and making management cumbersome. In this embodiment, by using software to estimate the near-floor temperature in the perimeter zone P, it is possible to reduce the cost of installing sensors and control the airflow from the variable air volume device 5 based on the near-floor temperature.

As explained above, this embodiment employs a control method using a temperature correction value, and this control method can be conceptualized as shown in the block diagram of Fig. 11. First, in a variable air volume air conditioning system (see Fig. 1), the direct control target is the temperature of the indoor air A2 near the ceiling 3 (ceiling temperature _T1 ) (shown as block B1 in Fig. 11), which is grasped as a measurement value of the ceiling temperature sensor 9 installed near the ceiling 3 (block B2). This measurement value is compared (block B4) with the target ceiling temperature _T1SP (block B3) to calculate the deviation (control deviation) between them, and the variable air volume device 5 (block B5) adjusts the damper opening (block B6) to eliminate this control deviation, thereby adjusting the amount of indoor air A2 supplied.

Furthermore, the air conditioner 1 (block B7), which supplies conditioned supply air A1 to the variable air volume device 5 (block B5), adjusts the fan air volume (block B8) according to the required air volume of the variable air volume device 5, and sends out an appropriate volume of supply air A1 to the downstream variable air volume device 5 (block B6). Additionally, the air conditioner 1 (block B7) adjusts the opening of the refrigerant flow path (block B9) using load reset control to adjust the temperature of supply air A1.

In this way, the ceiling temperature _T1 (block B1), which is the object of control, is adjusted through the air volume and temperature of supply air A1. Ceiling temperature _T1 (block B1) is further affected by disturbances (block B10), but the resulting ceiling temperature _T1 (block B1) is detected by ceiling temperature sensor 9 (block B2) and compared with the ceiling temperature target value _T1SP (block B3; see equation (3) above) (block B4), and control is performed in a direction that also eliminates fluctuations due to disturbances.

The control method using the temperature correction value can be said to be cascade control in which another loop (the loop outside the area surrounded by the dashed line; referred to as the major loop) is added to surround the loop formed by blocks B1 to B10 (referred to as the minor loop; shown as the area surrounded by the dashed line in Figure 11).

That is, a near-floor temperature _T2 (block C1) is set as another control target related to the ceiling temperature _T1 (block B1), and this is estimated using the temperature estimation model M. The output near-floor temperature estimated value _T2E (block C2) is compared with the near-floor temperature target value _T2SP (block C3; see equation (4) above) (block C4). A temperature correction value β (block C5) calculated based on the deviation between these two values (control deviation _E2 ) is then added to the ceiling temperature target value _T1SP set in block B3 (see equation (3) above). Within the minor loop, the temperature of the indoor air A2 near the ceiling 3 (ceiling temperature _T1 ; block B1) is adjusted based on the ceiling temperature target value _T1SP (block B3) that has been added with the temperature correction value β added, thereby adjusting the temperature of the indoor air A2 near the floor (near-floor temperature _T2 ; block C1). The near-floor temperature _T2 (block C1) is also affected by the disturbance (block C6), but the control by the major loop containing the minor loop works in the direction of eliminating this disturbance (block C6) by PI control via the temperature correction value. That is, when a disturbance (block C6) occurs due to a change in the exterior load, the near-floor temperature estimated value _T2E (block C2) changes, which causes a deviation (control deviation) _E2 from the near-floor temperature target value _T2SP (block C3), and the temperature correction value β as an manipulated variable changes in the direction of canceling out the deviation _E2 and is input to the minor loop.

This type of control method makes it possible to optimally correct the temperature situation near the floor by adding a temperature correction value based on the temperature near the floor to the target set temperature, while minimizing the need to modify an established variable air volume air conditioning system (minor loop).

This type of cascade control also offers advantages in terms of controllability. If a control configuration were adopted that eliminated disturbances (blocks B10 and C6) using a single loop, taking into account the data acquisition cycle in the central monitoring device 12 and the time required to estimate the near-floor temperature, the control cycle would be at least five minutes, resulting in large deviations calculated within the loop. Therefore, as in this embodiment, cascade control is configured consisting of a minor loop that controls the air volume of the variable air volume device 5 and the supply air temperature of the air conditioner 1, and an outer major loop that controls the near-floor temperature estimate. While the control cycle of the major loop is approximately five minutes, the control cycle of the minor loop is several seconds to several tens of seconds, resulting in small deviations calculated in block B4. Because the disturbances input to the minor loop (block B10) are controlled within the minor loop, they have little impact on the control variables of the major loop. In this way, the effects of disturbances can be effectively suppressed, improving controllability.

Note that the system configuration of the air conditioning system, the generation of the temperature estimation model M, the estimation of the near-floor temperature, and the procedures for calculating the temperature correction value described here are merely examples. As long as the temperature estimation model M can be generated from the various parameters related to the near-floor temperature as described above, the near-floor temperature can be estimated using this model, and the operation of the variable air volume device 5 can be adjusted, the system configuration and various procedures can be modified in various ways.

For example, while the configuration illustrated here is one in which the temperature estimation unit 13 is connected to the central monitoring device 12, and the model generation unit 14 is connected to the temperature estimation unit 13, the connection relationships between the devices that make up the system can be changed as appropriate. In addition to controlling the operating status of each air volume variable device 5 via the central monitoring device 12, for example, the temperature estimation unit 13 may directly set a temperature correction value for each air volume variable device 5. Furthermore, instead of correcting the set temperature, the blown air volume may be directly corrected.

Of course, when controlling the airflow rate from the variable airflow device 5, the supply air temperature from the air conditioner 1, and the operating conditions of other components, other factors not described above may also be taken into account as appropriate.

Here, we will explain how to estimate the amount of solar radiation used as a parameter. As explained above, the air conditioning system of this embodiment uses solar radiation to control the air conditioning, and it is naturally important to grasp as accurate a value as possible for solar radiation. Solar radiation can be measured using pyranometer 100 (see Figure 3). However, as shown in Figure 3, for example, if there are obstructions such as those indicated by symbols O1 and O2 around pyranometer 100, shadows may be cast on pyranometer 100 by these obstructions O1 and O2, depending on the location of these obstructions O1 and O2 and the season, which may cause the measured amount of solar radiation to deviate from the actual amount of solar radiation.

The air conditioning system of this embodiment is therefore equipped with a solar radiation estimation unit 101 for estimating the amount of solar radiation, and under certain conditions, the estimated value calculated by the solar radiation estimation unit 101 is used as the amount of solar radiation instead of the actual value measured by the pyranometer 100. The estimation of the amount of solar radiation by this solar radiation estimation unit 101 is described below.

The graph in Figure 12 shows an example of the amount of solar radiation measured by the pyranometer when there is no obstruction around the pyranometer. Each curve in the graph shows the change over time in the amount of solar radiation observed on a different day (dates D1 to D3). It is assumed that the weather was sunny throughout the day on each measurement day. When there is no obstruction around the pyranometer, the amount of solar radiation changes in a mountain shape as shown by each curve.

However, as shown in Figure 3, if there are obstructions O1 and O2 around the pyranometer 100, a shadow will be cast at the position of the pyranometer 100, which may affect the actual measured value of solar radiation at the pyranometer 100. Figure 13 shows an example of fluctuations in the actual measured value of solar radiation in such a case. Note that here, obstructions O1 and O2 are assumed to be, for example, other buildings or elevator shafts, but of course other devices, structures, terrain, etc. can also act as obstructions.

For example, on date D1, the sun is high enough in the sky that the amount of solar radiation is not affected by surrounding objects and exhibits the same fluctuations as when there is no obstruction (see Figure 12). However, on another date, D2, which is closer to the winter solstice than date D1, a shadow cast by obstruction O1 falls on the position of pyranometer 100 in the afternoon (here, from around 1:30 PM to around 2:00 PM), reducing the amount of solar radiation measured. On another date, D3, which is even closer to the winter solstice, a shadow cast by obstruction O2 falls on the position of pyranometer 100 in the morning (here, from around 8:30 AM to around 9:30 AM), reducing the amount of solar radiation measured. Furthermore, the time period during which the shadow cast by obstruction O2 falls is extended in the afternoon (from around 1:00 PM to 2:40 PM).

During the time when the sun casts a shadow on the pyranometer 100 (hereinafter, in this specification, the time when the sun casts a shadow on the target position is referred to as the "shadow time"), the actual amount of solar radiation is, for example, as shown by the dashed line in Figure 13. However, due to the influence of the obstructions O1 and O2, the measured value during this shadow time will be less than the actual amount of solar radiation.

Therefore, using the method described below, during shadow hours, the amount of solar radiation is estimated as close as possible to the actual value, as shown by the dashed line in Figure 13, and this estimated value is used as the amount of solar radiation to operate the air conditioning system.

The solar constant is defined as the energy received per unit area and time by a surface perpendicular to sunlight at the top of the atmosphere (at a height of approximately 8 km above the Earth's surface) when the Earth is at an average distance (r) from the Sun, and is approximately 1,367 [W/m ² ]. The extraatmospheric global solar radiation Q0 corresponding to a certain point on Earth (longitude λ, latitude φ) at a certain time (month a, day b, c hour, d _minute ) can be calculated by correcting this solar constant according to the distance (r ^* ) from the Sun of the target point at the desired time and the angle the ground at the target point makes with respect to sunlight (solar altitude α; a value different from α in equations 1 to 4 above; the same applies to δ and k mentioned below). In other words, the extraatmospheric global solar radiation _Q0 can be expressed by the following equation:
Q ₀ = 1367 (r/r ^* ) ² sin (α) ... (I)

The geocentric solar distance r/r ^* (the ratio of the distance r ^* between the Earth and the Sun at the target time to the average distance r) and solar altitude α in the above formula (I) can be calculated by the following procedure.

First, the angle θ ₀ moved by revolution from New Year's Day to the current day is calculated using the following formula: dn is the number of consecutive days from New Year's Day, and can be calculated simply from the date (month a, day b).
θ ₀ =2π(dn-1)/365...(II)

Using the angle θ ₀ calculated by the above formula (II), the solar declination δ, the geocentric solar distance r/r ^*, and the equation of time Eq on that day can be calculated.
δ=0.006918-0.399912cos(θ ₀ )+0.070257sin(θ ₀ ) -0.006758cos(2θ ₀ ) +0.000907sin(2θ ₀ ) -0.002697cos(3θ ₀ ) +0.001480 sin (3θ ₀ ) ...(III)
r/r ^* =1/√{1.000110+0.034221cos(θ ₀ ) +0.001280sin(θo) +0.000719cos(2θ ₀ )+0.000077sin(2θ ₀ )} ...(IV)
Eq=0.000075+0.001868cos(θ ₀ )-0.032077sin(θ ₀ )-90.014615cos(2θ ₀ )-0.040849sin(2θ ₀ ) ...(V)

Furthermore, the solar hour angle h can be calculated from the equation of time Eq and the longitude difference between standard time and the meridian using the following formula:
h = (standard time - 12) π/12 + longitude difference from the standard meridian + Eq (VI)

From the values thus obtained, the solar azimuth ψ and solar altitude α at a certain time and location can be calculated using the following formulas.
α = arcsin {sin (φ) sin (δ) + cos (φ) cos (δ) cos (h)} ... (VII)
ψ=arctan[cos(φ)cos(δ)sin(h)/{sin(φ)sin(α)-sin(δ)}]...(VIII)

The solar altitude α and the geocentric solar distance r/r ^* are calculated by the above calculations, and by substituting these values into the above formula (I), the extra-atmospheric global solar radiation _Q0 can be obtained. This extra-atmospheric global solar radiation _Q0 is the amount of solar radiation (energy received per unit area and time) received by a virtual surface outside the atmosphere corresponding to the target point at the target time.

The value obtained by adding the attenuation due to passage through the atmospheric layer to this extra-atmospheric global solar radiation _Q0 is treated as the solar radiation Q. The solar radiation Q is a function of the extra-atmospheric global solar radiation _Q0 , and specifically, can be calculated as an estimated value using the following formula.
Q=kQ ₀ ...(IX)

That is, when it is desired to estimate the amount of solar radiation at a certain point at a certain time, the extra-atmospheric global solar radiation _Q0 is first calculated based on the above formulas (I) to (VIII), and then this is multiplied by a separately determined coefficient k to obtain the estimated solar radiation value Q. In this way, the amount of solar radiation can be estimated simply and accurately.

Here, k is a coefficient representing attenuation due to passage through the atmospheric layer, and can be calculated as a value obtained by dividing the actual solar radiation measured at a certain point in time by the theoretical extra-atmospheric global solar radiation _Q0 at the same point in time.

In air conditioning systems such as those shown in Figures 1 to 3, when an estimated value based on the above formula (IX) is used as the amount of solar radiation, whether to adopt this estimated value during operation at a given time and what value to use for coefficient k can be determined as needed based on weather and other conditions. For example, when the weather is clear and no shadow is cast on pyranometer 100 by obstructions O1 and O2, the actual measured value from pyranometer 100 can be used as the amount of solar radiation. Furthermore, when the weather is cloudy or rainy, most of the solar radiation is scattered light, and the impact of direct solar radiation is not significant. Therefore, the impact of shading is relatively small, so it may not be necessary to use an estimated value depending on the application. In such cases, the amount of solar radiation can be estimated only during a shadowed time and when the weather is clear, and the estimated value can be used as the amount of solar radiation only when the impact of shading on the measured amount of solar radiation is significant. Of course, even when the weather is cloudy or rainy, if the impact of shading is a concern, the estimated value based on the above formula (IX) can also be used.

Even when using the above formula (IX), the value of coefficient k can be changed depending on the conditions. For example, if the value estimated by the above formula (IX) is used as the amount of solar radiation when the weather is cloudy, a different value should be used for coefficient k than when the weather is sunny.

Furthermore, even if the weather is the same (sunny), the method of calculating the coefficient k in the above formula (IX) or the value used as the coefficient k may be changed depending on the time period. As described above, the value of the coefficient k can be calculated by dividing the actual measurement value at a certain time by the extra-atmospheric global solar radiation _{Q0. Therefore} , for example, in a case where a shadow is cast on the pyranometer 100 immediately after a certain time _t0 on a sunny day, during the time period from time _t1 to _t2 , the actual measurement value at time _t0 divided by the extra-atmospheric global solar radiation _Q0 at the same time t0 can be set as the coefficient k, and the solar radiation Q from time _t1 to _t2 can be calculated as an estimated value based on the above formula (IX). However, according to research by the present inventors, it has been empirically found that the effectiveness of the estimated value obtained by such an operation varies depending on the time period. Specifically, the above-described operation can accurately estimate the amount of solar radiation during the afternoon hours, but if the same method is used to estimate the amount of solar radiation during the morning hours, the deviation of the estimated value from the actual value (the amount of solar radiation measured separately in a location without shade) will increase over time.

This is thought to be due to the fact that in the morning, the sun is low in the sky at the start of shadow time, making it susceptible to the effects of atmospheric absorption and scattering. Morning shadow time begins, for example, around 8:00 a.m., at which point the sun is low in the sky and sunlight travels a long distance through the atmosphere before reaching the position of pyranometer 100. For this reason, if coefficient k is calculated using the above formula (IX) based on the actual measured value of solar radiation just before the start of shadow time, the value of coefficient k will be significantly lower than 1. However, as time passes after shadow time begins, the sun's position rises higher, and the ratio of actual solar radiation to extra-atmospheric global solar radiation becomes larger (approaching 1) than coefficient k calculated just before the start of shadow time. Therefore, if the coefficient k calculated immediately before the start of the shadow period continues to be used throughout the shadow period thereafter, the estimated amount of solar radiation will deviate from the actual value (dashed line), as shown by the dashed-dotted line in Figure 14, for example. (Note that Figure 14 is a graph showing an example of the relationship between the amount of solar radiation measured by pyranometer 100, the actual amount of solar radiation, and the estimated amount of solar radiation, with the measured amount of solar radiation shown by the solid line and the actual amount of solar radiation shown by the dashed line. The dashed-dotted line and the double-dashed line each represent estimated values calculated using a different coefficient k.) This effect is particularly significant in seasons when shadow periods are long, when the sun's altitude is low throughout the day and sunrise is late.

For morning shadow time, therefore, a preset value is used as coefficient k, rather than using coefficient k calculated based on the actual measured value immediately before the start of shadow time. This makes it easier to estimate solar radiation with relatively good accuracy, as shown by the two-dot chain line in Figure 14, for example. When using a preset value for coefficient k in this way, coefficient k can be calculated as the average ratio of the actual measured value of solar radiation on a clear day outside of shadow time to the extra-atmospheric global solar radiation, for example. When calculating coefficient k in this way, it is possible to perform this calculation monthly, for example, and use a different value for coefficient k each month. The specific value for coefficient k may vary depending on the time of day, season, etc., but is generally around 0.65 to 0.75.

On the other hand, for afternoon shadow time, the amount of solar radiation is estimated using coefficient k calculated based on the actual measured value at the time immediately before the start of the shadow time. This allows for accurate estimation of the amount of solar radiation in accordance with the actual solar radiation conditions in the morning and afternoon.

It is also thought that the same effect will be greater with the afternoon shadow time as the time progresses (the ratio of actual solar radiation to extraterrestrial solar radiation will become smaller (moving further away from 1) than the coefficient k calculated just before the start of the shadow time), so in such cases, a pre-set value can also be used as the coefficient k.

An example of a specific procedure for estimating solar radiation using the above formula (IX) will be described with reference to the flowchart in Figure 15. Figure 15 illustrates a procedure for determining the weather at the beginning of a daylight period (for example, approximately 1.5 hours after sunrise) and then determining subsequent operations related to solar radiation based on this.

The solar radiation estimation unit 101 first acquires the current time (step S101) and determines whether the acquired current time is the weather determination time (step S102). Here, the "weather determination time" is, for example, a pre-set time that is "sufficiently early in the sunshine period, when the sun is high enough in the sky to allow the weather to be determined from the solar radiation, and when no shadow is cast on the pyranometer 100." Specifically, it is, for example, 1.5 hours after sunrise. Here, "sufficiently early in the sunshine period" refers to "a time between sunrise and that time when the solar radiation is low even if the weather is clear, and when the solar radiation measurement value obtained by the pyranometer 100 deviates from the actual solar radiation, thereby minimizing the impact of solar radiation on system operation." A time period that meets the above condition of "a sufficiently early time during the day when there is sunlight and when the sun is high enough in the sky to be able to judge the weather from the amount of solar radiation" is, for example, approximately one to two hours after sunrise, and a time within this time period at which it is known that no shadow will be cast at the position of the pyranometer 100 can be set as the weather judgment time. Note that "a time at which it is known that no shadow will be cast at the position of the pyranometer 100" can be determined empirically, for example, from actual shadow duration around the desired date and time. Alternatively, it can be determined by calculation from the movement of the sun at the desired date and time and the location of objects and structures around the pyranometer 100.

If it is determined in step S102 that the current time is the weather determination time, the weather at that time is determined as the weather for that day (step S103). The weather can be determined, for example, by the amount of solar radiation measured by the pyranometer 100. If the amount of solar radiation is equal to or greater than a certain threshold (for example, 200 W/ ^m2 ), it can be determined as sunny, and if it is less than the threshold, it can be determined as cloudy or rainy. Once the weather has been determined, the process returns to step S101.

If it is determined in step S102 that it is not time to determine the weather, a further determination is made of the weather determination result (step S104). If step S102 has already been performed for that day and the weather is clear when step S104 is performed, the process proceeds to step S106. However, if step S102 has not yet been performed for that day, or if step S102 has been performed but the weather is not clear, the process proceeds to step S105.

In step S105, it is decided to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and the process returns to step S101.

In step S106, it is determined whether the current time is in a shadow time. This determination is made by referring to the shadow time data input in advance to the solar radiation amount estimation unit 101 in the form shown in Table 1 below.

While shadow time can be accurately calculated, for example, from the movement of the sun at a given date and time and the location of objects and structures around the pyranometer 100, the setting of shadow time does not need to be very accurate, at least when estimating solar radiation for the purpose of operating an air conditioning system. As shown in Table 1 above, it is sufficient to roughly grasp the time periods during which the pyranometer 100 is shaded in a certain season and set a wide shadow time to cover that entire period. In this way, there will be time periods during which the pyranometer 100 is not actually shaded but which are set to be shadow time. During such time periods, even though the pyranometer 100 is not shaded (and therefore, using the actual measured value as the amount of solar radiation does not have any effect from the shadow), an estimated value will be used as the amount of solar radiation rather than an actual measured value. However, this does not pose any significant problem as long as the estimated value does not deviate significantly from the actual amount of solar radiation. Conversely, if the set shadow time is shorter than the actual shadow time, there will be times when the actual measured value is used instead of the estimated value for solar radiation, even though the shadow affects the measured value (actual shadow time), which may cause problems in understanding the amount of solar radiation.

Note that the above Table 1 is merely an example, and it goes without saying that the number of times a day that the sun is in the shade may be greater or less than this depending on the conditions around the pyranometer. Also, while the above example shows a table that sets the amount of shade time by month, it is also possible to use data that sets the amount of shade time by week or day, for example.

If the current time is not in the shadow period, proceed to step S105, decide to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and return to step S101. If the current time is in the shadow period, proceed to step S107.

In step S107, it is determined whether the current time is in the morning. If it is in the morning, the process proceeds to step S108, where it is determined that a predetermined value to be used as the coefficient for the morning shadow time is to be used as the coefficient k (see formula (IX) above). The solar radiation estimator 101 then multiplies the extra-atmospheric global solar radiation _Q0 at that time by the value of this coefficient k (see formulas (I) to (VIII) _above for the method of calculating the extra-atmospheric global solar radiation Q0) to calculate an estimated solar radiation value Q (step S109). Once the calculation is complete, the process returns to step S101.

If it is determined in step S107 that the current time is not morning, the process proceeds to step S110, where a coefficient k to be used to estimate the amount of solar radiation during the subsequent shadow period is calculated. The coefficient k can be calculated by dividing the actual measured value of solar radiation immediately before the start of the shadow period by the extra-atmospheric global solar radiation _Q0 . Note that the "actual measured value of solar radiation" used to calculate the coefficient k here is preferably not a measured value at a single point in time, but an average value of measured values over a certain period of time (for example, the average value of solar radiation measured in the 10 minutes immediately before the shadow period). This is to reduce the influence of noise on the estimated value of solar radiation. The calculated value of coefficient k is multiplied by the extra-atmospheric global solar radiation _Q0 at that time to calculate the estimated value Q of solar radiation (step S111). After calculation, the process returns to step S101.

In the procedure shown in Figure 15 above, the weather is determined only once a day at a predetermined early time (1.5 hours after sunrise). Because the weather determination can be performed based on the measurements of the pyranometer 100, there is no need for an operator to input the day's weather into the solar radiation estimation unit 101 or to use information such as a weather forecast obtained from an external source. Instead, solar radiation according to the weather can be estimated with a minimum number of steps using only the information obtained by the pyranometer 100.

Another example of the procedure for estimating the amount of solar radiation using the above formula (IX) will be described with reference to the flowchart in Figure 16. Figure 16 illustrates the procedure for determining the weather during each shadow period and determining subsequent operations related to solar radiation based on this.

In this procedure, the solar radiation estimation unit 101 acquires the current time (step S121), and then first determines whether the current time corresponds to a shadow time (step S122). The shadow time can be determined by referencing the current time in data such as that shown in Table 1 above, but may also be determined in other ways.

If the current time is not in the shadow period, proceed to step S123, decide to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and return to step S121. If the current time is in the shadow period, proceed to step S124.

In step S124, it is determined whether the current time is morning. If it is morning, the process proceeds to step S125, where the weather is determined. The weather determination here is performed, for example, by referring to weather forecast data.

If the weather is not clear, proceed to step S123, decide to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and return to step S121. If the weather is clear, proceed to step S126.

In step S126, a predetermined value is determined as the coefficient k (see formula (IX) above) to be used for the morning shadow time. The solar radiation estimator 101 then multiplies the extra-atmospheric global solar radiation _Q0 at that time by the value of this coefficient k to calculate an estimated solar radiation value Q (step S127). Once the calculation is complete, the process returns to step S121.

If it is determined in step S124 that the current time is not morning, the process proceeds to step S128, where the weather is determined. The weather determination here is performed by referring to, for example, weather forecast data or data entered by the worker as the current weather.

If the weather is not sunny, proceed to step S123, decide to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and return to step S121. If the weather is sunny, proceed to step S129.

In step S129, a coefficient k to be used in estimating the amount of solar radiation during the subsequent shadow period is calculated. The coefficient k can be calculated by dividing the actual measured value of solar radiation immediately before the start of the shadow period by the extra-atmospheric global solar radiation _Q0 . The calculated value of coefficient k is multiplied by the extra-atmospheric global solar radiation _Q0 at that time to calculate an estimated value Q of solar radiation (step S130). Once the calculation is complete, the process returns to step S121.

In the procedure shown in Figure 16 above, the weather is determined when calculating the amount of solar radiation during shadow time. This allows for operation that is more in line with actual weather conditions than the procedure shown in Figure 15 above, which determines the weather only once at the beginning of the day, and is particularly effective during seasons when the weather is changeable.

Another example of a procedure for estimating solar radiation will be described with reference to the flowchart in Figure 17. The procedure described in Figure 17 is generally similar to the procedure in Figure 15, but in the procedure in Figure 17, a decision is made based on the weather as to whether to use an estimated value or an actually measured value for solar radiation for morning shadow time, while an estimated value is used for afternoon shadow time regardless of the weather.

The process from obtaining the current time to determining the weather is the same as the procedure shown in Figure 15. First, the current time is obtained (step S101), and it is determined whether the obtained current time is the time to determine the weather (step S102). If it is the time to determine the weather, the weather at that time is determined (step S103). Once the weather has been determined, the process returns to step S101.

If it is determined in step S102 that it is not time to determine the weather, the procedure first determines whether the current time is in shadow (step S134). If the current time is not in shadow, it is decided to use the actual measured value of the pyranometer 100 as the amount of solar radiation (step S105), and the process returns to step S101.

If it is currently in shadow time, the process then proceeds to determine whether it is morning (step S136). If the current time is morning, then the current point in time is in shadow time and morning. In this procedure, the weather is determined at this stage (step S137).

If the weather is determined to be clear in step S137, proceed to step S138 and determine to use a predetermined value as the coefficient to be used for morning shadow time as coefficient k. The solar radiation estimation unit 101 multiplies the extra-atmospheric global solar radiation Q0 at that time by the value of coefficient k to calculate the estimated solar radiation value Q (step S139). Once the calculation is complete, return to step S101.

If it is determined in step S137 that the weather is not clear, the process proceeds to step S105, where it is decided to use the actual measured value of the pyranometer 100 as the amount of solar radiation, and the process returns to step S101.

If it is determined in step S136 that it is not morning, the current time is in the shadow and afternoon. In this case, proceed to step S140.

In step S140, the coefficient k used to estimate the amount of solar radiation during the subsequent shadow period is calculated. Here, coefficient k is calculated by dividing the actual measured value of solar radiation immediately before the start of the shadow period by the extra-atmospheric global solar radiation Q0. Here, the "actual measured value of solar radiation" used to calculate coefficient k can be the average value of measurements over a certain period of time. The calculated value of coefficient k is multiplied by the extra-atmospheric global solar radiation Q0 at that time to calculate the estimated amount of solar radiation Q (step S141). Once calculated, return to step S101.

In this procedure, estimated values are used as insolation during morning shadow time only if the weather is determined to be clear, and estimated values are used as insolation during afternoon shadow time regardless of the weather determination. Because weather changes from moment to moment, if the weather determination for that day is performed only once in the early morning, the reliability of the weather determination result will decrease over time. Therefore, in the procedure shown in Figure 17, the weather determination result is referenced for morning shadow time, and the operation related to insolation is changed depending on the weather, but the weather determination result is not referenced for afternoon shadow time, and estimated values are used as insolation regardless of whether the weather is clear. Note that even if the weather is not clear, the coefficient k can be calculated from the extraatmospheric global solar radiation Q0 and the actual measured value of insolation, and it is possible to use this to calculate an estimated value of insolation during shadow time (although the value of coefficient k used in this case will be smaller than the value of coefficient k for clear weather). When it is cloudy or rainy, the amount of solar radiation fluctuates greatly because the clouds block the solar radiation, but the influence of direct solar radiation on the amount of solar radiation is not zero. Therefore, as explained above, by calculating the coefficient k based on the actual measured value of solar radiation obtained at that time each time, and then calculating an estimated value of solar radiation based on this, it is possible to estimate the amount of solar radiation with a certain degree of accuracy even when it is cloudy or rainy.

Note that the three procedures described here are merely examples. When implementing the solar radiation estimation method of the present invention, appropriate modifications may be made, such as changing the content or order of each step, omitting some steps, or adding other steps. For example, the above describes a case in which solar radiation is not estimated unless the weather is determined to be clear, and actual measured values are used as solar radiation. However, if it is desirable to minimize the effects of shading even on cloudy or rainy days, solar radiation may be estimated during shadow time using a coefficient k corresponding to the weather, even when the weather is cloudy or rainy. Furthermore, depending on the start and end times of each shadow time, coefficient k may be calculated from actual measured values of solar radiation during morning shadow time, or a predetermined coefficient k may be used during afternoon shadow time. Furthermore, while the above describes a procedure for estimating solar radiation in real time, it is possible to envision applications in which retrospective estimation of solar radiation is sufficient. In such cases, the order of each step may of course be significantly changed.

This method of estimating solar radiation uses estimated values instead of actual measured values during times when the pyranometer 100 is in the shade, eliminating the influence of shading and allowing for a solar radiation amount that is close to the actual value. The estimated solar radiation amount can be calculated by a simple calculation based on extra-atmospheric global solar radiation, and is extremely simple, as no special equipment other than the pyranometer 100 is required.

To eliminate the effect of shadows on pyranometer measurements, it would be best to install the pyranometer in a location where shadows are not created, but this can be difficult. For example, various devices are often installed on the roofs of office buildings, and there is often no space available to install a pyranometer in a location where shadows are not created. In such cases, it is theoretically possible to install a structure such as a platform on which to install the pyranometer, but this would naturally incur additional costs. Another possible solution would be to install multiple pyranometers in different locations and use the actual measurements from one pyranometer during times when a shadow is created on another pyranometer, but installing multiple pyranometers naturally incurs considerable costs. According to the method or system described in this embodiment, it is possible to easily estimate solar radiation with a minimal configuration and eliminate the effect of shadows on the pyranometer, while minimizing the need for additional structures, devices, or complex calculations.

By following the above procedure, the amount of solar radiation (rooftop solar radiation) at the installation location of the pyranometer 100 can be calculated by appropriately using an estimated value as the amount of solar radiation during shadow hours, thereby minimizing the effects of shadowing as much as possible and obtaining an amount of solar radiation that is close to the correct value. Next, we will explain the specific method for applying the amount of solar radiation (actually measured value or estimated value) obtained in this way to air conditioning control at each location in the perimeter zone P on each floor.

When using the rooftop solar radiation to generate the temperature estimation model M by the model generation unit 14 or to estimate the near-floor temperature at each location using the temperature estimation model M, it is advisable to take into account the shadow time at each location and use the rooftop solar radiation accordingly.

The impact of solar radiation on a certain location in a building varies greatly depending on whether or not there is solar radiation at that location, i.e., whether the location is in the sun or shade. The presence or absence of solar radiation also varies depending on the direction of the location, the number of floors in the building, the location on the floor, and the season and time of day. Therefore, if you want to estimate some kind of situation related to solar radiation at a certain location in a space to be air-conditioned, you can make a highly accurate estimate of the impact of solar radiation by taking into account the presence or absence of solar radiation at the target location in addition to the amount of solar radiation on the rooftop determined using the above method.

In the air conditioning system of this embodiment described above, if it is desired to estimate the near-floor temperature at each location at a certain time while taking into account the amount of solar radiation, for example, the shadow time at each location can be determined, and based on this, the presence or absence of solar radiation at each location at each time can be compiled as data as shown in Table 2 below. That is, in addition to the amount of solar radiation determined using the above method, the presence or absence of solar radiation at the target location can be determined using shadow time data as shown in Table 2 below, and this can be used as an explanatory variable in the temperature estimation model M. Note that in Table 2 below, the names written in the top row (5F 1-1-1, 36F 4-2-3, etc.) correspond to each location on each floor, and "RF" refers to the rooftop. The left column shows each time of year in five-minute increments. At a certain location (for example, 1-1-1 on the 6th floor) at a certain time (for example, 10:00 on April 1st or 9:00 on June 12th), if there is sunlight (the location is in the sun), the value is defined as "1," and if there is no sunlight (the location is in the shade), the value is defined as "0."

By referring to Table 2 above, the shadow duration at a target location on a target floor in a building to be air-conditioned can be determined, and the presence or absence of solar radiation at a given time can be input into the temperature estimation model M as a variable of either "1" or "0." That is, in the temperature estimation model M, for example, if there is solar radiation at a target location at a given time, a "1" from the data in Table 2 above is input as the explanatory variable indicating the presence or absence of solar radiation. Based on this, the temperature estimation model M calculates the near-floor temperature by performing some kind of processing, such as multiplying an appropriate value, an explanatory variable such as rooftop solar radiation (the amount of solar radiation actually measured or estimated as solar radiation by the pyranometer 100). Also, if there is no solar radiation at the target location, a "0" is input as the explanatory variable indicating the presence or absence of solar radiation. Based on this, the temperature estimation model M calculates the near-floor temperature by performing some kind of processing (a calculation process different from the case when the explanatory variable indicating the presence or absence of solar radiation is set to 1) on the explanatory variables such as rooftop solar radiation. Note that this explanation is a hypothetical example, and the actual processing performed based on each setting variable within the temperature estimation model M generated by machine learning will vary depending on the machine learning process and the specifications of the temperature estimation model M.

Furthermore, when using data regarding the presence or absence of solar radiation such as that shown in Table 2 above in the temperature estimation model M, rather than simply inputting the presence or absence of solar radiation into the temperature estimation model M in the form described above (either "1" or "0"), it is possible to input into the temperature estimation model M a value that has been previously calculated for the amount of solar radiation on the rooftop depending on the presence or absence of solar radiation. In addition, when using the amount of solar radiation on the rooftop or the presence or absence of solar radiation at each location in the temperature estimation model M, any appropriate method can be used as a specific method.

Data on the presence or absence of solar radiation at each location, such as that shown in Table 2 above, can be created, for example, based on a sky factor map for each location. A sky factor map (or sky map) is a diagram that shows the portion of the sky that an object, such as a building, occupies as seen from a certain measurement point. By creating a sky factor map, it is possible to determine the duration of shadowing for each day of the year based on the longitude and latitude of the measurement point and the position and shape of the object. Therefore, by creating a sky factor map for each target location, it is possible to create data on shadow duration and the presence or absence of solar radiation, such as that shown in Table 2 above.

However, in a high-rise building, for example, it would be extremely time-consuming to create sky exposure factor maps and determine the shadow time for all locations that are expected to be subject to air conditioning control. Therefore, for example, some floors among the air-conditioned locations could be designated as representative floors, and the shadow time at the location of these representative floors could be calculated based on the sky exposure factor map. For the shadow time on floors other than the representative floor, the shadow time on the representative floor could be used as is, or the shadow time on the representative floor could be adjusted in some way. This is because, even if the floors or locations are different, as long as the locations are close to each other, the shadow time can, in principle, be considered to be roughly the same.

For example, if floors 6 to 30 of a building are to be subject to air conditioning control, floors 8, 13, 18, 23, and 27 can be defined as representative floors, and the shadow time data for floor 8 can be used for floors 6 to 10, the shadow time data for floor 13 for floors 11 to 15, the shadow time data for floor 18 for floors 16 to 20, the shadow time data for floor 23 for floors 21 to 25, and the shadow time data for floor 27 for floors 26 to 30. Alternatively, for floors 9 to 12, interpolation can be performed based on the data for floors 8 and 13. For example, if the start time of morning shadow time at a certain location on the 8th floor on a certain day is 9:00, and the start time of morning shadow time at the corresponding location on the 13th floor on the same day is 9:30, the start times of morning shadow time on floors 9 to 12 may be interpolated proportionally between 9:00 and 9:30, and determined to be 9:06, 9:12, 9:18, 9:24, etc., respectively.

In addition, when there are a large number of locations on a single floor that are expected to be subject to air conditioning control, some locations can be designated as representative locations and the shadow time can be calculated, and for other locations, the shadow time at a nearby representative location can be used as is, or corrections such as interpolation based on the shadow time at multiple representative locations can be made.

Furthermore, while Table 2 above schedules the presence or absence of sunlight at each location in five-minute increments, it is also possible to calculate the shadow time for only some days and use this to determine the shadow time for other days, even when seasonal variations in shadow time occur. In principle, it is possible to accurately determine the sunlight schedule based on the sky exposure factor map for each location, but performing similar calculations for all 365 days that make up a year would still require a huge amount of calculation effort. Therefore, it is possible to calculate the shadow time for a limited number of days, such as the first day of each month, as a representative value, and then interpolate the shadow time for other days to determine the shadow time. For example, the shadow time for the first day of each month is calculated based on the sky exposure factor map, and the shadow time from April 2nd to April 30th can be obtained by interpolating between the shadow time schedule for April 1st and the shadow time schedule for May 1st.

Furthermore, if you try to calculate the amount of solar radiation for each day of the year, as in a general solar radiation simulation, you cannot use the data for a representative floor or representative day as explained above as the basis and apply it to other locations or days, and you have to perform all simulations individually every five minutes.As explained above, by setting the presence or absence of solar radiation as a variable separate from the amount of solar radiation and understanding this as a schedule, you can significantly reduce the amount of work required for calculations.
Table 3 below shows the results of examining the estimation accuracy of near-floor temperatures for several types of temperature estimation models constructed to use parameters selected from the parameters listed above as explanatory variables.

Model 1 is a temperature estimation model that uses the current outdoor air temperature, current solar radiation, the operating status of the perimeter air conditioner, and the current ceiling temperature (current measurements from ceiling temperature sensors installed at eight locations) as explanatory variables. Outdoor air temperature and solar radiation are major factors in the exterior load of the perimeter zone. The operating status of the perimeter air conditioner (the air conditioner that supplies intake air to the perimeter zone) naturally directly affects the air conditioning status in the perimeter zone. As described above, the ceiling temperature is the direct control target when controlling the near-floor temperature in the air conditioning system of this embodiment, and is correlated with the near-floor temperature. In Model 1, which uses these values as explanatory variables, the RMSE value, which indicates the estimation accuracy of the near-floor temperature, was 0.587, and the near-floor temperature could be estimated with sufficient accuracy for practical purposes.
In addition to the four explanatory variables used in Model 1, Model 2 also uses past ceiling temperature values (up to 30 minutes in 5-minute increments) as an explanatory variable. As mentioned above, control of the ceiling temperature affects the temperature near the floor with a time lag, so past ceiling temperature values are thought to have a particularly high correlation with the temperature near the floor. In Model 2, which uses past ceiling temperature values, the RMSE value is 0.564, which is even lower than Model 1, and is even more accurate than Model 1.
Model 3 uses moving average values of outdoor temperature and solar radiation (i.e., the average of the current value and multiple past values close to the present) as explanatory variables instead of the current values used in Model 1. Outdoor temperature and solar radiation significantly affect the load on the perimeter zone exterior, but there is a time lag between their changes and the resulting change in perimeter zone temperature. For this reason, past values are often more strongly correlated with the perimeter zone near-floor temperature than current values, and moving average values that also reflect past values are thought to have a stronger correlation than current values. Furthermore, values that tend to fluctuate significantly over a short period of time, such as solar radiation, are difficult to calculate using the gradient method used to determine appropriate weighting during machine learning using neural networks, while average values are easier to learn. Model 3, which uses these explanatory variables, had an RMSE of 0.576, again demonstrating higher accuracy than Model 1.
In addition to the four explanatory variables used in Model 1, Model 4 also uses the measured supply air temperatures of the perimeter air conditioners and interior air conditioners as explanatory variables. While it goes without saying that the supply air temperature supplied to the perimeter zone (the supply air temperature of the perimeter air conditioner) directly affects the temperature in the perimeter zone, the supply air temperature supplied to the interior zone (the supply air temperature of the interior air conditioner) also has a significant impact on the temperature in the perimeter zone. For example, in the interior zone of an office building, where people are likely to be active and many devices are operating, heating may be used in the morning shortly after people begin to be active and devices are operating, and cooling may be switched to in the afternoon once the indoor air has warmed. Furthermore, cooling and heating in the interior zone have significantly different effects on the air conditioning status in the perimeter zone. Therefore, in addition to the supply air temperature of the perimeter air conditioner, the supply air temperature of the interior zone air conditioner is also useful as an explanatory variable when estimating the temperature near the floor in the perimeter zone. In Model 5 using these explanatory variables, the RMSE value was 0.573, which also showed higher accuracy than Model 1.
In addition to the four explanatory variables used in Model 1, Model 5 also uses the amount of heat flowing outdoors as an explanatory variable. The heat balance in air conditioning is related to various heat transfers and generation, but among these values, the amount of heat flowing outdoors has a particularly large impact on indoor temperatures. Model 5, which uses this amount of heat flowing outdoors as an explanatory variable, had an RMSE value of 0.564, which also showed higher accuracy than Model 1.
Models 6 and 7 use moving average values for outside air temperature and solar radiation, just like Model 2. They also use the operating status of the perimeter air conditioners and interior air conditioners, their actual measured intake air temperatures, and the current values of outflow heat and ceiling temperature as explanatory variables. Models 6 and 7 also use the operating status of the variable air volume devices facing the perimeter zone as an explanatory variable. The operating status of the variable air volume devices facing the perimeter zone naturally has a direct effect on the temperature in the perimeter zone. Furthermore, Models 6 and 7 also use the presence or absence of solar radiation at multiple locations (Points 1 to 6) as an explanatory variable. As a result, the RMSE was 0.548 or 0.551, demonstrating particularly high accuracy compared to Models 1 to 5.
Furthermore, Model 6 in particular further uses past ceiling temperature values as an explanatory variable, but the RMSE value is even lower than Model 7, which does not use past ceiling temperature values (0.551 for Model 7, compared to 0.548 for Model 6). As mentioned above, changes in ceiling temperature affect the near-floor temperature with a time lag, so it is thought that using past ceiling temperature values as an explanatory variable in this way will enable near-floor temperature to be estimated with even greater accuracy.

As described above, the air conditioner comprises an air conditioner 1 that sends out supply air A1, an air supply duct 2 that guides the supply air A1 from the air conditioner 1 to the target space S, a plurality of outlets 4 that blow the supply air A1 into the target space S, a variable air volume device 5 that adjusts the volume of the supply air A1 blown out from one of the outlets 4 that supplies the air to the perimeter zone P, a perimeter fan 6 that is provided in the perimeter zone P and sends indoor air A2 upward, an intake 16 that is provided above the perimeter fan 6 and draws in at least a portion of the return air to the air conditioner, and a ceiling temperature sensor 9 that measures the ceiling temperature in the perimeter zone P. The air conditioner is configured to estimate the near-floor temperature at least at each position in different directions in the perimeter zone P based on parameters related to the near-floor temperature in the perimeter zone P, and to adjust the blowing air volume of the variable air volume device 5 facing the perimeter zone P based on the estimated near-floor temperature, and the estimation of the near-floor temperature is configured to be performed using at least the ceiling temperature sensed by the ceiling temperature sensor 9 as a parameter. In this way, the amount of air sent from the outlet 4 to the perimeter zone P is increased appropriately based on the near-floor temperature, and the supplied warm air reaches the perimeter fan 6 and is sent to the upper inlet 16, thereby blocking the cold air generated at the outermost perimeter of the perimeter zone P and preventing cold drafts from entering the interior. In this way, the temperature distribution near the floor of the target space S can be corrected. In this case, by using the ceiling temperature, which is the direct control target when controlling the near-floor temperature, as a parameter, the near-floor temperature can be accurately estimated.

The air conditioning system of this embodiment is also configured to estimate the near-floor temperature using at least past values of ceiling temperature detected by the ceiling temperature sensor 9 as parameters. In this way, by using past values of ceiling temperature, which have a significant influence on the current near-floor temperature value, as parameters, the near-floor temperature can be estimated with even greater accuracy.

The air conditioning system of this embodiment is also configured to estimate the near-floor temperature using the amount of solar radiation as a parameter, and the amount of solar radiation is understood as an estimated value calculated based on the amount of extra-atmospheric global solar radiation during the shadow time set as the time when a shadow is cast on the pyranometer 100, while at other times it is understood as an actual measured value by the pyranometer 100. In this way, when estimating the near-floor temperature using the amount of solar radiation as a parameter, by using the estimated value instead of the actual measured value during the shadow time, it is possible to eliminate the influence of shadowing and obtain a solar radiation amount that is close to the actual value.

Furthermore, in the air conditioning system of this embodiment, the estimated amount of solar radiation during shadow hours can be calculated by multiplying the extra-atmospheric global solar radiation during shadow hours by a preset coefficient, thereby making it possible to easily and accurately estimate the amount of solar radiation.

In addition, in the air conditioning system of this embodiment, the estimated value of solar radiation during the shadow period can be calculated by multiplying the extraterrestrial global solar radiation during the shadow period by a coefficient calculated by dividing the actual measured value of solar radiation before the shadow period by the extraterrestrial global solar radiation. This also makes it possible to easily and accurately estimate the solar radiation.

In addition, in the air conditioning system of this embodiment, the estimated amount of solar radiation during the morning shadow time is calculated by multiplying the extra-atmospheric global solar radiation during the shadow time by a preset coefficient, and the estimated amount of solar radiation during the afternoon shadow time is calculated by multiplying the extra-atmospheric global solar radiation during the shadow time by a coefficient calculated by dividing the actual measured amount of solar radiation before the shadow time by the extra-atmospheric global solar radiation. In this way, the amount of solar radiation can be accurately estimated in accordance with the actual solar radiation conditions in the morning and afternoon.

Furthermore, in the air conditioning system of this embodiment, the amount of solar radiation can be estimated under the conditions that it is a shadow time and the weather is determined to be clear. In this way, the estimated value can be used as the amount of solar radiation only when the influence of shadows on the amount of solar radiation is large.

Furthermore, in the air conditioning system of this embodiment, the near-floor temperature can be estimated using the presence or absence of solar radiation at the target location as an additional parameter. In this way, when estimating the near-floor temperature at the target location using the amount of solar radiation, a highly accurate estimation of the influence of the amount of solar radiation can be performed.

Furthermore, in the air conditioning system of this embodiment, the presence or absence of solar radiation can be determined based on the shadow time at the target location.

In addition, in the air conditioning system of this embodiment, the shadow time at a target location can be determined based on the sky factor map.

The air conditioning system of this embodiment is also configured to estimate the near-floor temperature in the perimeter zone P using a temperature estimation model M generated by machine learning. In this way, the near-floor temperature in the perimeter zone P can be estimated with high accuracy.

Furthermore, in the air conditioning system of this embodiment, the temperature estimation model M can be configured to estimate the near-floor temperature by further using some or all of the parameters selected from the following parameters as explanatory variables:
- Outside air temperature - Operating state of the air conditioner 1 - Amount of heat emitted - Wind direction - Wind speed - Rainfall amount - Measurement value of the near-floor temperature sensor 11 - Operating state of the perimeter fan 6 - Operating state of the variable air volume device 5 facing the perimeter zone P - Date - Time - Day of the week Furthermore, the air conditioning system of this embodiment can be configured so that the amount of solar radiation and the presence or absence of solar radiation at the target location are input into the temperature estimation model M as separate explanatory variables.

Therefore, according to the above embodiment, the air temperature near the floor in the perimeter zone can be suitably estimated.

The air conditioning system of the present invention is not limited to the above-described embodiment, and various modifications can of course be made without departing from the spirit of the present invention.

REFERENCE SIGNS LIST 1 Air conditioner 2 Intake duct 4 Outlet 5 Variable air volume device 6 Perimeter fan 9 Ceiling temperature sensor 11 Near-floor temperature sensor 16 Intake port 100 Pyranometer A1 Intake air A2 Indoor air M Temperature estimation model P Perimeter zone S Target space

Claims (14)

An air conditioner that sends out supply air,
an air supply duct that guides air from the air conditioner to a target space;
A plurality of air outlets that blow supply air into the target space;
an air volume variable device for adjusting the volume of supply air blown out from one of the air outlets that supplies air to the perimeter zone;
a perimeter fan provided in the perimeter zone for blowing indoor air upward;
an intake port provided above the perimeter fan for drawing in at least a portion of return air to the air conditioner;
a ceiling temperature sensor for measuring a ceiling temperature in the perimeter zone, and estimating the temperature near the floor at each position in at least different directions in the perimeter zone based on a parameter related to the temperature near the floor in the perimeter zone;
The airflow rate of the variable air volume device facing the perimeter zone is adjusted based on the estimated near-floor temperature,
the estimation of the near-floor temperature is configured to be performed using at least the ceiling temperature sensed by the ceiling temperature sensor as a parameter , and is further configured to be performed using the amount of solar radiation as a parameter;
The amount of solar radiation is grasped as an estimated value calculated based on the amount of extra-atmospheric global solar radiation during a shadow time set as a time when a shadow is cast on the pyranometer, while
The air conditioning system is characterized in that it is configured to grasp the actual measured value of the pyranometer at other times . An air conditioner that sends out supply air,
an air supply duct that guides air from the air conditioner to a target space;
A plurality of air outlets that blow supply air into the target space;
an air volume variable device for adjusting the volume of supply air blown out from one of the air outlets that supplies air to the perimeter zone;
a perimeter fan provided in the perimeter zone for blowing indoor air upward;
an intake port provided above the perimeter fan for drawing in at least a portion of return air to the air conditioner;
a ceiling temperature sensor for measuring the ceiling temperature in the perimeter zone;
The system is configured to estimate near-floor temperatures at least at positions in different directions in the perimeter zone based on parameters related to near-floor temperatures in the perimeter zone, and adjust the blow-out air volume of the variable air volume device facing the perimeter zone based on the estimated near-floor temperatures;
the estimation of the near-floor temperature is configured to be performed using at least the ceiling temperature detected by the ceiling temperature sensor as a parameter , and is configured to be performed using the presence or absence of solar radiation at the target position as a further parameter;
The presence or absence of solar radiation is determined based on the shadow time at the target location.
An air conditioning system characterized by: 3. The air conditioning system according to claim 1 , wherein the estimation of the near-floor temperature is performed using at least a past value of the ceiling temperature detected by the ceiling temperature sensor as a parameter. The estimation of the near-floor temperature is further configured to be performed using an amount of solar radiation as a parameter,
The amount of solar radiation is grasped as an estimated value calculated based on the amount of extra-atmospheric global solar radiation during a shadow time set as a time when a shadow is cast on the pyranometer, while
The air conditioning system according to claim 2 , wherein the system is configured to grasp the actual measured value of the pyranometer at other times. 5. The air conditioning system according to claim 1 , wherein the estimated value of the amount of solar radiation during the shadow time is calculated by multiplying the amount of extra-atmospheric global solar radiation during the shadow time by a preset coefficient. The air conditioning system according to any one of claims 1 and 4 to 5, characterized in that the estimated value of the amount of solar radiation during the shadow time is calculated by multiplying the amount of extraterrestrial global solar radiation during the shadow time by a coefficient calculated by dividing the actual measured value of the amount of solar radiation before the shadow time by the amount of extraterrestrial global solar radiation. The estimated value of solar radiation during the morning shadow time is calculated by multiplying the extraterrestrial global solar radiation during the shadow time by a preset coefficient,
5. The air conditioning system according to claim 1, wherein the estimated value of the amount of solar radiation during the afternoon shadow time is calculated by multiplying the amount of extraterrestrial global solar radiation during the shadow time by a coefficient calculated by dividing the actual measured value of solar radiation before the shadow time by the amount of extraterrestrial global solar radiation. The air conditioning system according to any one of claims 1 and 4 to 7 , wherein the amount of solar radiation is estimated under the conditions that it is a shadow time and the weather is determined to be fine. The air conditioning system according to any one of claims 1 and 3 to 8 , wherein the estimation of the near-floor temperature is configured to be performed using the presence or absence of solar radiation at the target position as a further parameter. The air conditioning system according to claim 9 , wherein the presence or absence of solar radiation is determined based on the duration of shadow at the target position. The air conditioning system according to claim 10 , wherein the shadow time at the target location is determined based on a sky factor map. The air conditioning system according to any one of claims 1 to 11 , characterized in that it is configured to estimate the temperature near the floor of the perimeter zone using a temperature estimation model generated by machine learning. 13. The air conditioning system according to claim 12 , wherein the temperature estimation model is configured to estimate the near-floor temperature by further using some or all of the following parameters as explanatory variables:
-Outside temperature -Operating status of the air conditioner -Amount of heat discharged -Wind direction -Wind speed -Rainfall -Measurement value of the temperature sensor near the floor that measures the temperature near the floor in the perimeter zone -Operating status of the perimeter fan -Operating status of the variable air volume device facing the perimeter zone -Date -Time -Day of the week 14. The air conditioning system according to claim 12 or 13 , wherein the amount of solar radiation and the presence or absence of solar radiation at the target position are input to the temperature estimation model as mutually separate explanatory variables.