JP6587353B2 - Control device, air conditioning control system, control method and program - Google Patents

Description

  The present invention relates to a control device, an air conditioning control system, a control method, and a program.

  There is a technique called ADR (Automated Demand Response) that adjusts the balance between power demand and supply by, for example, raising or lowering the electricity rate and suppressing the amount of power supply during peak hours. For example, in the United States, realization of power charges by OpenADR has been developed in the general market, and operation such as selecting a cheap power supply company in real time is possible. In Japan, there is a movement to develop the domestic version of OpenADR2.0 in the United States as an ADR standard under the leadership of the government.

  By the way, in an air conditioner, control which does not impair a user's comfort is requested | required, suppressing electric power consumption and suppressing an electric power charge cheaply. For example, Patent Document 1 describes a device management system that predicts the comfort level, power consumption, and the like when the operation state of a device is changed for a device such as an air conditioner and displays a prediction result.

Japanese Patent No. 5491891

  In the conventional air conditioner, a control method has been pursued on the premise that the electric power charge does not change for a certain period, and the comfort is not impaired under the condition and the electric power charge is suppressed. However, no control method has been provided so far that achieves a desired comfortable temperature under a condition where the power rate changes in real time while keeping the power rate low.

  Then, this invention aims at providing the control apparatus, air-conditioning control system, control method, and program which can solve the above-mentioned subject.

A first aspect of the present invention includes a charge information acquisition unit that acquires information on a unit price of electric power per unit time in a predetermined period in the future, an operation information acquisition unit that acquires operation state information related to the operation state of the air conditioner , on the basis of the driving state information and a predetermined prediction model, a prediction unit for predicting the operating state of the air conditioner in the predetermined time period, the evaluation of comfort over the predetermined period based on the operating state where the prediction unit predicts An evaluation value related to the operation of the air conditioner over the predetermined period is calculated based on the value and an evaluation value of the electric power charge necessary for the operation of the air conditioner over the predetermined period based on the information on the unit price of the electric power charge per unit time control comprising an evaluation unit for, and a control information calculation unit for calculating a control information for the air conditioner for evaluation value is operated such that the optimum values for operation of the air conditioner It is a device.

The control information according to the second aspect of the present invention is an upper limit value for each time having a length equal to or shorter than the unit time of electric power necessary for operating the air conditioner in the predetermined period.

The control information calculation unit according to the third aspect of the present invention is configured to increase the strength of operation of the air conditioner before the time when the power charge unit price indicated by the information on the power charge unit price per unit time increases. Is calculated.

Fourth put that before Symbol control information calculation unit to an aspect of the present invention, when the air conditioner is performing cooling operation, the temperature of the air conditioner provided space is lower than the set temperature set When the control information for increasing the rotation speed of the compressor included in the air conditioner so as to reach the temperature is calculated and the air conditioner is performing the heating operation, the temperature of the space provided with the air conditioner is set. Control information for increasing the rotational speed of the compressor included in the air conditioner so as to reach a temperature higher than the set temperature is calculated.

Fifth evaluation value before Symbol comfort that put the aspect of the present invention, the temperature difference between the air-conditioning temperature by setting set point temperature and the air conditioner, the capacity of the indoor unit in which the air conditioner is provided, the product of It is a value based on.

The evaluation unit in the sixth aspect of the present invention, the temperature difference between the air-conditioning temperature by the set temperature which is set the air conditioner, the power unit price and the temperature difference set weighting coefficients in correspondence with the To calculate the comfort evaluation value.

  A seventh aspect of the present invention is an air conditioning control system including any of the control devices described above and an air conditioner that operates based on control information calculated by the control device.

According to an eighth aspect of the present invention, there is provided a step of acquiring information on a unit price of electric power per unit time in a predetermined future period, a step of acquiring operating state information relating to an operating state of the air conditioner , the operating state information and a predetermined amount based on the prediction model, and predicting the operation state of the air conditioner in the predetermined time period, the evaluation value comfort over the predetermined period based on the operating state of the predicted power rates per unit time calculating the evaluation value of the power rate required for the operation of the air conditioner over the predetermined period based on the unit price information, an evaluation value relating to the operation of the air conditioner over a predetermined period based on the, relating to operation of the air conditioner Calculating control information for the air conditioner for operating so that an evaluation value becomes an optimum value.

According to a ninth aspect of the present invention, there is provided the computer of the control device, the means for acquiring the information on the unit price of power per unit time in the future predetermined period , the means for acquiring the operating state information regarding the operating state of the air conditioner , based on the state information and a predetermined prediction model, means for predicting the operating state of the air conditioner in the predetermined time period, the evaluation value comfort over the predetermined period based on the predicted operating state, each of the unit time means for calculating the evaluation value of the power rate required for the operation of the air conditioner over the predetermined period based on information of the power unit price, an evaluation value relating to the operation of the air conditioner over a predetermined period based on the, the air conditioner means for calculating the control information for the air conditioner for evaluation value is operated such that the optimum values for operation, program der to function as .

  According to the present invention, it is possible to control a device that achieves both economy and comfort in response to a power rate that changes from moment to moment.

It is a figure showing an example of an air-conditioning control system in a first embodiment of the present invention. It is a block diagram which shows the structural example of the control apparatus in 1st embodiment of this invention. It is a figure which shows an example of the electric power charge schedule information in 1st embodiment of this invention. It is a figure which shows an example of the driving | operation limitation schedule information in 1st embodiment of this invention. It is a 1st figure explaining the electric power limit driving | operation in 1st embodiment of this invention. It is a 2nd figure explaining the electric power limit driving | operation in 1st embodiment of this invention. It is a figure which shows the example of a penalty setting with respect to the deviation from preset temperature in 1st embodiment of this invention. It is a figure which shows an example of the search range setting of the electric power limit value in 1st embodiment of this invention. It is a flowchart which shows an example of the process of the control apparatus in 1st embodiment of this invention. It is a block diagram which shows the structural example of the control apparatus in 2nd embodiment of this invention.

<First embodiment>
Hereinafter, an air conditioning control system according to a first embodiment of the present invention will be described with reference to FIGS.
FIG. 1 is a diagram showing an example of an air conditioning control system in the first embodiment of the present invention.
The air conditioning control system 100 is a control system for operating the air conditioner 31 and the like provided in the building 4 and the like at a low cost while maintaining the user's comfort. The air conditioning control system 100 includes a DRAS server 1, a DRAS client 2, a gateway 3, an air conditioner 31, an air conditioner 32, and an air conditioner 3m. The DRAS server 1 is a server terminal device operated by an electric power company, for example. The electric power company changes the unit price of the electric power charge, for example, every 10 minutes. For example, if a power shortage due to power demand is anticipated, the power company will increase the unit price of the electric power to encourage consumers to save electricity, and reduce the unit price of the electric power tariff when the demand for power is expected to be relatively low. To encourage power consumption. The DRAS server 1 and the DRAS client 2 are communicably connected, and perform communication based on a publicly disclosed protocol (for example, OpenADR2.0). For example, the DRAS server 1 transmits power rate schedule information for a predetermined period in the future to the DRAS client 2.

  The DRAS client 2 is a server terminal device operated by a company that operates the air conditioner 31 or the like, for example. The DRAS client 2 is an operation restriction that indicates what operation can be performed based on the schedule information of the power rate acquired from the DRAS server 1 to operate the air conditioner at a low power rate while realizing the set temperature of the user. Schedule information is calculated. The DRAS client 2 is communicably connected to the gateway 3 via a network such as the Internet. The DRAS client 2 transmits the calculated operation restriction schedule information to the gateway 3.

The gateway 3 is a communication device provided in the building 4. The building 4 is provided with multi-type air conditioners 31, 32, 3m having a plurality of indoor units. The air conditioner 31 includes an outdoor unit 301, indoor units 311, 31n, and the like. The air conditioner 32 includes an outdoor unit 302, indoor units 321, 32n, and the like. The air conditioner 3m includes an outdoor unit 30m, indoor units 3m1, 3mn, and the like. For example, the outdoor unit 301 includes a compressor, a heat exchanger, and the like, and sends the refrigerant to the indoor units 311 and 31n. The indoor unit 311 and the like are provided in a room of the building 4 and perform air conditioning so that the temperature of the room becomes a temperature desired by the user.
The gateway 3 outputs the operation restriction schedule information for each air conditioner acquired from the DRAS client 2 to the corresponding air conditioner 31 and the like. The air conditioner 31 controls the operation of the own device based on the acquired operation restriction schedule information.

FIG. 2 is a block diagram illustrating a configuration example of the control device according to the first embodiment of the present invention.
The configuration of the DRAS client 2 will be described with reference to FIG. The DRAS client 2 includes a driving information acquisition unit 21, a fee information acquisition unit 22, a control information calculation unit 23, a driving state prediction unit 24, an evaluation unit 25, a communication unit 26, and a storage unit 27.
The operation information acquisition unit 21 acquires operation information of the air conditioners 31, 32, 3m, and the like. The operation information includes, for example, power consumption due to operation of the air conditioner 31, indoor temperature during operation, set temperature, outdoor temperature, and the like. The operation information acquisition unit 21 acquires the operation information of the air conditioners 31, 32, 3 m and the like at every predetermined time, and records the acquired operation information in the storage unit 27.
The charge information acquisition unit 22 acquires the schedule information of the power charge transmitted by the DRAS server 1. The schedule information of the power charge will be described later with reference to FIG.

  The control information calculation unit 23 calculates operation restriction schedule information that realizes the operation of the air conditioner 31 or the like that minimizes the power rate while maintaining the user's comfort using a predetermined optimization method. The predetermined optimization method is, for example, a method such as simulated annealing, a genetic algorithm, or particle swarm optimization. In addition, the driving restriction schedule information that minimizes the power charge while maintaining the user's comfort refers to the driving restriction schedule when the evaluation value output by the evaluation unit 25 described later is the smallest value. In the case of this embodiment, the operation restriction schedule information is an upper limit value of power consumed by each of the air conditioners 31, 32, and 3m every predetermined time.

  The operation state prediction unit 24 predicts the operation state of the air conditioners 31, 32, 3m, etc. under the conditions indicated by the operation restriction schedule information. For example, the operation state prediction unit 24 predicts the operation state of the air conditioner 31 several minutes ahead based on the current operation state of the air conditioner 31 acquired by the operation information acquisition unit 21. For example, the operation state prediction unit 24 predicts the operation state of the air conditioner 31 that is several minutes ahead based on the operation state of the air conditioner 31 that is several minutes ahead based on the current operation state. For example, a machine learning algorithm called a neural network is used for predicting the driving state.

The evaluation unit 25 evaluates the operation state of the air conditioner 31 and the like ahead of the predetermined time predicted by the operation state prediction unit 24 using an evaluation function described later. More specifically, the evaluation unit 25 includes a power rate evaluation unit 251 and a comfort evaluation unit 252, and the driving state prediction unit 24 has two aspects of power rate (cost) and user comfort. Evaluate the operation of the air conditioner 31 and the like in the predicted future.
The communication unit 26 communicates with other devices. For example, the communication unit 26 performs data communication with the DRAS server 1 and acquires schedule information of power charges. Alternatively, the communication unit 26 performs data communication with the gateway 3 and transmits the operation restriction schedule information to the gateway 3.
The storage unit 27 stores various data necessary for calculating the operation restriction schedule information. For example, the storage unit 27 stores a prediction model that the driving state prediction unit 24 uses for prediction of the driving state. This prediction model is a prediction model generated by a technique such as a neural network on the basis of past driving state performance information when certain driving restriction schedule information is given. In this prediction model, when the power limit schedule information, the actual power consumption, the room temperature, and the outdoor temperature are input, the predicted future room temperature in the future ahead of the predetermined time and the power consumption consumed so far are output. .

Next, power charge schedule information will be described.
FIG. 3 is a diagram showing an example of power rate schedule information in the first embodiment of the present invention. The power charge schedule information includes a power charge unit price for each predetermined time (for example, 10 minutes). In the case of the example in FIG. 3, the power charge per kWh is 30 yen at 14:00:00 to 14:10 today, 10 yen at 14: 10-14: 20, and 100 yen at 14: 20-14: 30. Yes. For example, the DRAS server 1 transmits, to the DRAS client 2, schedule information including a power charge unit price every 10 minutes up to 30 minutes every time 10 minutes elapses. In the DRAS client 2, the charge information acquisition unit 22 acquires the schedule information of the power charge unit price via the communication unit 26 and records it in the storage unit 27.

Next, the driving restriction schedule information will be described.
FIG. 4 is a diagram showing an example of the operation restriction schedule information in the first embodiment of the present invention. The operation restriction schedule information includes a power restriction value every predetermined time (for example, 5 minutes).
This power limit value is the air conditioner 31 for operating the air conditioner 31 or the like at the lowest power charge while achieving the indoor set temperature set by the user as much as possible in the case of the unit price of the power charge illustrated in FIG. This is the upper limit value of the power consumed.
In the case of the example of FIG. 4, in the time zone (14: 0 to 14:10) where the unit price is 30 yen, the first half is 14 kW and the second half is 7 kW. Subsequently, in the time zone (14: 10-14: 20) where the unit price is 10 yen, the first half is 3 kW and the second half is 7 kW. Finally, in the time zone (14: 20-14: 30) where the unit price is 100 yen, the first half and the second half are 0 kW.
In the DRAS client 2, the operation state prediction unit 24 predicts the operation state of the air conditioner 31 and the like up to 30 minutes after the operation restriction schedule calculated by the control information calculation unit 23, and the evaluation unit 25 performs the 30 minutes. Evaluate electricity charges and comfort by driving. And the control information calculation part 23 resets more suitable driving | operation limitation schedule information based on the evaluation result by the evaluation part 25. FIG. For example, when the power charge is high even though the comfort is good, the control information calculation unit 23 suppresses the power limit value of the driving limit schedule and resets the conditions for driving with lower power. In addition, for example, when the power rate is very low but the comfort is low, the control information calculation unit 23 increases the power limit value of the operation limit schedule, and the setting set by the user even if the power rate relatively increases Resetting conditions to improve comfort by realizing temperature. The schedule information of the power limited operation illustrated in FIG. 4 is the power limit value for each predetermined time optimized in this way.

  Next, the evaluation function used by the evaluation unit 25 will be described. The following formula (1) is an example of the evaluation function of this embodiment.

Here, α ₁ in equation (1) is a weighting factor for power rate evaluation, α ₂ is a weighting factor for comfort evaluation, Y _j is a unit price of electric power for j unit time, and W _j is power consumption of device for j unit time. The amount (Wh), T _setji is the user set temperature (° C.) of the indoor unit i during j unit time, and _Troomji is the room temperature (suction temperature) of the indoor unit i during j unit time (° C.) ), Cap _i is the device capacity (kW) of the indoor unit i, Total_Capacity is the total device capacity value (kW) of all the indoor units in operation, i is the identification number of the indoor unit in operation, i = (1, 2, ... Imax), j is a unit time number, j = (1, 2,... Jmax). The power rate evaluation unit 251 calculates the evaluation value of the power rate according to the equation of the first term on the right side of the equation (1), and the comfort evaluation unit 252 calculates the comfort value according to the equation of the second term on the right side of the equation (1). An evaluation value is calculated. Since the first term on the right side of the equation (1) is an equation for summing the power charges per unit time over a predetermined period, this value increases as the power charge increases. On the other hand, in the second term, one air conditioner further includes a value obtained by multiplying the square of the temperature difference between the temperature set by the user and the actual indoor temperature (air conditioning temperature) by the indoor unit capacity for each indoor unit per unit time. Since the sum of the values divided by the sum of the capacities of all indoor units over a predetermined period is added to all the indoor units, the larger the difference between the set temperature set by the user and the actual indoor temperature, the larger the value. It becomes. In other words, the lower the comfort, the larger the value. Therefore, the lower the power charge and the higher the comfort, the smaller the value of equation (1). The control information calculation unit 23 optimizes the schedule information of the power limited operation so that the evaluation value according to the expression (1) calculated by the evaluation unit 25 is as small as possible.

Note that W _{j in} the first term on the right side of Equation (1) may be only the amount of power consumed by the outdoor unit, or may be the total amount of power consumed by the outdoor unit and all indoor units. . It can be expressed as follows using a mathematical formula.
W _j = P _ouj (2)

Here, P _ouj is the electric energy (Wh) consumed by the outdoor unit in j unit time, and P _IUji is the electric energy (Wh) consumed by the indoor unit i in j unit time.
Next, a process for optimizing schedule information for power limited operation will be described.

FIG. 5 is a first diagram illustrating the power limiting operation in the first embodiment of the present invention.
FIG. 6 is a second diagram illustrating the power limiting operation in the first embodiment of the present invention.
5 and 6, the vertical axis indicates power consumption (kW), and the horizontal axis indicates time (minutes). In the case of FIG. 6, the vertical axis further indicates the power limit value (kW).
The graph 51 shown in FIG. 5 shows the time transition of the power consumption when the air conditioner 31 is operated by the normal control (control that does not provide the operation limit by the power limit value of the present embodiment). A graph 61 shown in FIG. 6 shows a time transition of the power consumption when the air conditioner 31 is operated by the control with the operation restriction by the power restriction value of the present embodiment. A graph 62 shows an operation restriction schedule (time transition of the power restriction value) for the air conditioner 31 calculated by the control information calculation unit 23. 5 and 6, the user's set temperature in the indoor units 311 to 31n is 25 ° C. Further, it is assumed that air conditioning is started at time 0 under substantially the same temperature conditions.

First, the operation state prediction unit 24 performs 5 minutes based on the latest operation state information (power limit value, actual power, indoor temperature, set temperature, outdoor temperature) of the air conditioner 31 acquired by the operation information acquisition unit 21 and the prediction model. The operation state of the air conditioner 31 for every minute is predicted. Next, the operation state prediction unit 24 uses the operation state information and the prediction model of the air conditioner 31 after 5 minutes predicted by the operation state, and the air conditioner 31 of every minute until the next 5 minutes (that is, from 5 minutes to 10 minutes). Predict driving conditions. The driving state prediction unit 24 repeats the same prediction in units of 5 minutes 6 times in total, and simulates the driving state up to 30 minutes after time 0 as a reference. In these six simulations, an arbitrary value is given as the power limit value.
Next, the evaluation unit 25 substitutes the 30-minute driving state information (T _setji is 25 ° C.) simulated by the driving state prediction unit 24 into the equation (1) to calculate an evaluation value. Note that constants such as α ₁ , α ₂ , Cap _i , and Total_Capacity are set in advance and recorded in the storage unit 27. Further, the power charge schedule information acquired by the charge information acquisition unit 22 is used as the hourly power charge unit price Y _j .

Next, the control information calculation unit 23 optimizes the power limit value every 5 minutes based on the evaluation value calculated by the evaluation unit 25. For example, for a period of 5 minutes from the start (first simulated section), a higher power limit value is set to achieve the target set temperature, and the operation of the air conditioner 31 is promoted. In the fourth simulation section (15 to 20 minutes), the following is a section where the unit price of electricity is as high as 100 yen, so it is strengthened in advance to suppress driving in the high section (5 to 6th simulation section). The power limit value is set in accordance with the fluctuation of the power unit price and the deviation (comfort) between the set temperature and the room temperature, such as keeping the room cool.
However, since the power limit value only defines the upper limit of the power consumption, even if the control information calculation unit 23 sets a higher power limit value, the air conditioner 31 depends on the higher power limit value. While the unit price of electricity is cheap, it does not always operate with increasing driving intensity. Therefore, for such a situation, the control information calculation unit 23 calculates control information for increasing the operating intensity of the air conditioner 31 in the simulated section before the time when the power unit price increases. Specifically, when the air conditioner 31 is performing the cooling operation, the control information calculation unit 23 darkly sets a temperature (for example, 23 ° C.) lower than the set temperature 25 ° C., and reaches the temperature. Control information for increasing the rotational speed of a compressor (not shown) included in the outdoor unit 301 is calculated. Similarly, when a heating operation is performed, a temperature higher than the set temperature is set to dark, and control information for increasing the rotation speed of the compressor so that the room temperature reaches that temperature is calculated. It is assumed that the rotational speed of the compressor corresponding to the target temperature of the room set in the dark is determined in advance and recorded in the storage unit 27. As described above, the control information calculation unit 23 calculates the operation restriction schedule information including the power restriction value and the control information of the rotation speed of the compressor according to the situation.

Next, the operation state prediction unit 24 obtains new operation restriction schedule information (power limit value for each time, or power limit value for each time and the rotation speed of the compressor) for the six sections set by the control information calculation unit 23. Using again, the operation state of the air conditioner 31 in the future 30 minutes is simulated. In addition, the evaluation unit 25 evaluates the simulation result using the evaluation function E of Expression (1). Further, the control information calculation unit 23 calculates operation restriction schedule information that minimizes the evaluation function E using an optimization method such as an annealing method. If the operation restriction schedule information for every 30 minutes is calculated every 5 minutes, the control information calculation unit 23 executes 6-dimensional space exploration using the power restriction values for a total of 6 times as a solution.
When such an operation restriction schedule information is repeatedly optimized and the operation restriction schedule that minimizes the value of the evaluation function E is determined, the control information calculation unit 23 obtains the optimized operation restriction schedule information as the communication unit 26. To the gateway 3 via. The gateway 3 transmits the optimized operation restriction schedule information to the air conditioner 31, and the air conditioner 31 operates so as not to exceed the voltage limit value every 5 minutes indicated by the operation restriction schedule information (sometimes the compressor is turned on). Rotate faster). As a result of verifying such control in an actual environment, it was possible to realize an operation in which the electricity charge was reduced while sacrificing some room temperature. It should be noted that by adjusting the values of α ₁ and α _{2 in} the formula (1), it is possible to arbitrarily adjust which of the comfort and the power charge is to be put into operation.

Next, a setting example of the coefficient α ₂ that affects the comfort evaluation value will be described.
When the coefficient α ₂ is set most simply, α ₂ = 1−α ₁ is set. Thereby, the trade-off between economy and comfort can be adjusted.
Further, when it is desired to reflect the degree of deviation from the set temperature in the evaluation value, for example, a penalty for deviation from the set temperature is set as shown in FIG.
Coefficient alpha ₂ of the evaluation function as shown in FIG. 7 may be varied depending on the degree of deviation and the power unit price from the set temperature. For example, in order to perform an operation that allows a slight overcooling during cooling operation, even when the same 3 ° C. divergence is caused by 3.0 ° C. This can be realized by setting a larger value to α ₂ . In the figure, the setting example in the case where the unit price of the electricity charge during the cooling operation is 10 yen is a setting based on such an idea. In other words, even if the temperature is the same, the penalty value is larger when it is deviated to the + side (room temperature is higher than the set temperature) than when it is deviated to the-side (room temperature is lower than the set temperature). Is set.
In addition, when the power unit price is high, it is possible to perform a setting that allows it even if it is somewhat hot. For example, even if the deviation is the same + 3 ° C., it can be realized by setting a smaller value when the unit price is 100 yen (“5”) than when the unit price is 10 yen (“10”).

In addition, as shown in FIG. 7, during the heating operation, for example, it is possible to set the same penalty for a state where the warming is not too warm and the warming is insufficient. In the example of FIG. 7, the same value is set even if the temperature is deviated to the + side or the − side if the same temperature deviates in the setting during the heating operation.
In addition, when the unit price of power charge is high, it is possible to perform an operation that allows it even if it is somewhat cold. For example, as shown in FIG. 7, even when the deviation is the same + 3 ° C., the unit price is set to a smaller value when the unit price is 100 yen (“5”) than when the unit price is 10 yen (“10”). Driving can be realized.
The data table illustrated in FIG. 7 may be prepared for each user (for each indoor unit) together with the setting interface so that the user can freely set the data table.

FIG. 8 is a diagram illustrating an example of setting a search range of the power limit value in the first embodiment of the present invention.
By the way, the operation control schedule information of the present embodiment realizing both economy and comfort must be sequentially calculated during the operation of the air conditioner. In order for the control information calculation unit 23 to sequentially calculate the optimal operation control schedule information in accordance with the power rate unit price that changes in real time, it is necessary to shorten the time for finding the optimal solution. The data table shown in FIG. 8 defines limits to be added to the search range and search unit (resolution) when the control information calculation unit 23 finds the optimum power limit value by the annealing method. For example, according to the setting value in the first row, if the outdoor unit 301 is an outdoor unit with a relatively small capacity of 4 HP, the search range of the power limit value is limited to 0 kW to 15 kW, and the search unit is limited to every 5 kW. Is shown. By using the search range table in this way, the search time can be reduced, and an optimal solution can be derived without performing a useless area search. Note that the search range and the search unit can be adjusted according to the capability of calculation processing.

FIG. 9 is a flowchart showing an example of processing of the control device in the first embodiment of the present invention.
The flow of operation restriction schedule information generation processing will be described with reference to FIG.
First, the charge information acquisition unit 22 acquires the power charge unit price schedule information generated by the DRAS server 1 (step S11) and records it in the storage unit 27. The power charge schedule information includes, for example, a power charge unit price every 10 minutes up to 30 minutes ahead.
Next, the operation information acquisition unit 21 acquires operation state information of the air conditioners 31, 32,..., 3m (step S12). The operation state information includes information such as the power consumption per unit time in units of the air conditioner 31, the outside air temperature, the room temperature in units of indoor units, the temperature set by the user, and the like. The driving information acquisition unit 21 records the acquired driving state information in the storage unit 27.

  Next, the control information calculation unit 23 calculates driving restriction schedule information for every predetermined time (for example, every 5 minutes) in a future predetermined period (for example, 30 minutes) (step S13). For example, the control information calculation unit 23 may set an arbitrary initial value as a power limit value every 5 minutes for 30 minutes at the first time. Or the initial setting of the driving | running | working restriction | limiting schedule information according to driving | running conditions is previously recorded on the memory | storage part 27, and the control information calculation part 23 respond | corresponds to electric power rate schedule information, outside temperature, the indoor temperature of each indoor unit, etc. Information on initial setting of power rate schedule information suitable for these operating conditions may be read from the storage unit 27, and the read initial setting information may be set as a power limit value every 5 minutes over 30 minutes.

  Next, the driving | running state prediction part 24 estimates the driving | running state for every predetermined time in the future predetermined period (step S14). More specifically, the driving state prediction unit 24 inputs the driving state information acquired in step S12 and the driving restriction schedule information set in step S13 to the prediction model, and performs air conditioning every minute in the future 5 minutes. Predict actual power consumption per unit and indoor temperature per unit. Next, the operation state prediction unit 24 predicts the actual power consumption and the room temperature from the operation state information acquired in step S12 and the operation restriction schedule information set in step S13 by the prediction model. The information replaced with the predicted value later is input to the prediction model, and the actual power consumption in units of air conditioners every minute until 5 minutes ahead (5 minutes from the start time to 10 minutes later) The amount and the indoor temperature of each indoor unit are predicted. Similarly, the prediction every 5 minutes is repeated 6 times in total, and the actual power consumption for 30 minutes and the indoor temperature are predicted.

Next, the evaluation unit 25 calculates an evaluation value for the driving state predicted by the driving state prediction unit 24 (step S15). Specifically, since the actual power consumption per minute and the predicted value of the indoor temperature for each indoor unit are calculated by the prediction in step S14, the evaluation unit 25 uses the equation (1) to perform air conditioning. The evaluation value is calculated for each machine. At this time, the evaluation unit 25 (comfort evaluation unit 252) determines the weighting coefficient α according to the unit price of the electric power rate and the allowable temperature deviation and the direction of the deviation (whether the temperature is higher or lower than the target set temperature). ₂ may be changed to calculate an evaluation value related to comfort (see FIG. 7). Note that α ₁ , α ₂ , Cap _i , Total_Capacity, the data table related to α ₂ illustrated in FIG. 7, and the like are recorded in the storage unit 27 in advance. The evaluation unit 25 outputs the calculated evaluation value to the control information calculation unit 23.

  Next, the control information calculation unit 23 determines whether or not the evaluation value has converged to the minimum value (step S16). The evaluation value convergence determination is performed, for example, by the repeated calculation of the evaluation value reaching a specified number of times or by comparing with a specified threshold value. When it has converged to the minimum value (step S16; Yes), the control information calculation unit 23 outputs the operation restriction schedule information for each air conditioner 31 or the like when the evaluation value is minimized to the gateway 3 (step S17). The gateway 3 outputs the operation restriction schedule information to the corresponding air conditioners 31 to 3m, and the air conditioners 31 to 3m operate for the next 30 minutes according to the operation restriction schedule information. As a result, it is possible to reduce the electricity charge necessary for the operation of the air conditioner while achieving the set temperature desired by the user as much as possible and maintaining comfort.

  If it has not converged to the minimum value (step S16; No), the processing from step S13 is repeated until the evaluation value converges. Specifically, the control information calculation unit 23 calculates new operation restriction schedule information that minimizes the evaluation value by using an optimization method such as an annealing method (step S13). At this time, in order to shorten the calculation time, for example, the power limit value may be calculated with reference to a data table that limits the search range and search unit illustrated in FIG. Moreover, you may include the control information which raises the rotation speed of a compressor as needed in operation restriction schedule information.

According to the present embodiment, it is possible to operate the air conditioner while reducing the power charge while satisfying the user's comfort even in an environment where the power charge unit price fluctuates.
In the above example, the case where the function for calculating the driving restriction schedule information is implemented in the DRAS client 2 has been described as an example. However, the present invention is not limited to this. For example, these functions may be implemented in the gateway 3.
Note that the driving state prediction unit 24 has the driving state information acquired by the driving information acquisition unit 21 at a certain time, the driving state information acquired by the driving information acquisition unit 21 after 5 minutes, and the driving restriction schedule set during that time. Information may be acquired and the prediction model recorded in the memory | storage part 27 may be updated continuously using machine learning techniques, such as a neural network.

<Second embodiment>
Hereinafter, an air conditioning control system according to a second embodiment of the present invention will be described with reference to FIG.
In the second embodiment, humidity, weather information, human sensor information, and the like are further used for comfort evaluation.
FIG. 10 is a block diagram illustrating a configuration example of the control device according to the second embodiment of the present invention.
Among the configurations according to the second embodiment of the present invention, the same components as those constituting the DRAS client 2 according to the first embodiment of the present invention are denoted by the same reference numerals, and description thereof is omitted. The DRAS client 2a according to the second embodiment includes a sensor information acquisition unit 28 and a weather information acquisition unit 29 in addition to the configuration of the first embodiment. Further, the DRAS client 2 a includes an evaluation unit 25 a and a comfort evaluation unit 252 a instead of the evaluation unit 25 and the comfort evaluation unit 252.

The sensor information acquisition part 28 acquires the measured value which the human sensor or image sensor (camera) provided in the room with which each indoor unit was equipped, and the humidity sensor measured.
The weather information acquisition unit 29 acquires weather data from a weather data server terminal device (not shown) that stores weather data. The meteorological data includes forecast values for daily maximum and minimum temperatures.

The comfort evaluation unit 252a evaluates comfort using an evaluation formula that takes into account the external temperature deviation coefficient, the humidity coefficient, the density coefficient, and the like in addition to the evaluation items of the first embodiment.
The evaluation unit 25a calculates the value of the evaluation function E by adding the evaluation value calculated by the power rate evaluation unit 251 and the evaluation value calculated by the comfort evaluation unit 252a.

Next, a comfort calculation method by the comfort evaluation unit 252a will be described.
The comfort evaluation value by the comfort evaluation unit 252a of the second embodiment can be calculated by the following mathematical formula.
(When cooling)

(When heating)

The outside temperature deviation coefficient is defined below.
(When cooling)
Outside temperature deviation coefficient = β1 × (maximum temperature-room temperature) (6)
(When heating)
Outside temperature deviation coefficient = β1 × (Indoor temperature-minimum temperature) (7)
Here, β1 is an arbitrary constant. The maximum temperature and the minimum temperature are the daily maximum temperature forecast value and the minimum temperature forecast value acquired by the weather information acquisition unit 29, respectively. However, when (maximum temperature-room temperature) becomes a negative value during the cooling operation, the comfort evaluation unit 252a sets β1 = 0. Similarly, when the heating operation (indoor temperature−minimum temperature) becomes a negative value, the comfort evaluation unit 252a sets β1 = 0. The indoor temperature is, for example, the latest indoor temperature.

The humidity coefficient is defined below.
Humidity coefficient = β2 × {100 ÷ (100−humidity)}
Here, β2 is an arbitrary constant. The humidity is a measured value of the humidity of each room acquired by the sensor information acquisition unit 28.

The density coefficient is defined below.
Density coefficient = β3 × Density Here, β3 is an arbitrary constant. Further, the density is a value corresponding to the number of people present in each room detected by the human sensor or the image sensor acquired by the sensor information acquisition unit 28. For example, a data table in which the detection values of human sensors and the like for each room are associated with the degree of congestion is recorded in the storage unit 27, and the comfort evaluation unit 252a includes the detection values of the human sensors and the data. The density is calculated based on the table.

  According to the present embodiment, air conditioning control can be performed according to a more precise comfort index that takes into account humidity, maximum / minimum temperature, crowd density, and the like.

  Each process in the above-described DRAS clients 2 and 2a is stored in a computer-readable recording medium in the form of a program, and this program is read and executed by the computer of the DRAS clients 2 and 2a. The above processing is performed. Here, the computer-readable recording medium means a magnetic disk, a magneto-optical disk, a CD-ROM, a DVD-ROM, a semiconductor memory, or the like. Alternatively, the computer program may be distributed to the computer via a communication line, and the computer that has received the distribution may execute the program. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, what is called a difference file (difference program) may be sufficient.

  In addition, it is possible to appropriately replace the components in the above-described embodiments with known components without departing from the spirit of the present invention. The technical scope of the present invention is not limited to the above-described embodiment, and various modifications can be made without departing from the spirit of the present invention. For example, in the present embodiment, the control of the air conditioner has been described as an example, but the present invention can also be applied to other devices (such as a refrigerator and a heat pump). The DRAS clients 2 and 2a are examples of control devices.

DESCRIPTION OF SYMBOLS 100 ... Air-conditioning control system 1 ... DRAS server 2, 2a ... DRAS client 3 ... Gateway 31, 32, 3m ... Air conditioner 4 ... Building 301, 302, 30m ... Outdoor Unit 311, 31n, 321, 32n, 3m1, 3mn ... Indoor unit 21 ... Driving information acquisition unit 22 ... Charge information acquisition unit 23 ... Control information calculation unit 24 ... Driving state prediction unit 25 , 25a: Evaluation unit 251: Electricity rate evaluation unit 252, 252a ... Comfort evaluation unit 26 ... Communication unit 27 ... Storage unit 28 ... Sensor information acquisition unit 29 ... Weather Information acquisition unit

Claims (9)

A charge information acquisition unit that acquires information on the unit price of power charges per unit time in a predetermined period in the future ;
An operation information acquisition unit for acquiring operation state information related to the operation state of the air conditioner ;
On the basis of the driving state information and a predetermined prediction model, a prediction unit for predicting the operating state of the air conditioner in the predetermined time period,
Wherein the evaluation value of comfort that prediction unit spans the predetermined period based on the operating condition expected, the evaluation of power rate required for the operation of the air conditioner over the predetermined period based on information of the power unit price per unit time An evaluation unit that calculates an evaluation value related to the operation of the air conditioner over the predetermined period based on the value, and
A control information calculation unit that calculates control information for the air conditioner to operate so that an evaluation value related to the operation of the air conditioner is an optimal value;
A control device comprising: The control information is an upper limit value for each time having a length equal to or shorter than the unit time of electric power required to operate the air conditioner in the predetermined period.
The control device according to claim 1. The control information calculation unit calculates control information for increasing the strength of the operation of the air conditioner before the time when the power charge unit price indicated by the information on the power charge unit price per unit time increases.
The control device according to claim 1 or 2. Before SL control information calculation unit, when the air conditioner is performing cooling operation, the temperature of the air conditioner provided space, said air conditioner so as to reach a temperature lower than the set temperature set When the control information for increasing the rotation speed of the compressor provided is calculated and the air conditioner is performing heating operation, the temperature of the space in which the air conditioner is provided reaches a temperature higher than the set temperature. Calculating control information for increasing the rotational speed of the compressor included in the air conditioner,
The control device according to claim 3. Evaluation value before Symbol comfort is the temperature difference and the capacity of the indoor unit in which the air conditioner is provided, based on the product value of the air-conditioning temperature by setting set point temperature and the air conditioner,
The control device according to any one of claims 1 to 4. The evaluation unit includes a temperature difference between the air-conditioning temperature by a preset temperature which is set the air conditioner, the power and the unit price, the evaluation of the comfort of the weighting coefficient set by multiplying the temperature difference associated with the Calculate the value,
The control device according to any one of claims 1 to 5. The control device according to any one of claims 1 to 6,
An air conditioner that operates based on control information calculated by the control device;
An air conditioning control system. Obtaining information on unit price of electricity per unit time in a predetermined period in the future ;
Obtaining operational state information relating to the operational state of the air conditioner ;
Based on said operation state information and a predetermined prediction model, and predicting the operation state of the air conditioner in the predetermined time period,
Wherein the evaluation value comfort over the predetermined period based on the predicted operating state, the evaluation value of the power rate required for the operation of the air conditioner over the predetermined period based on information of the power unit price per unit time, Calculating an evaluation value related to operation of the air conditioner over the predetermined period based on:
Calculating control information for the air conditioner for operating the evaluation value related to the operation of the air conditioner to be an optimum value;
A control method. Control device computer,
Means for acquiring information on the unit price of electricity per unit time in a predetermined period in the future ,
Means for obtaining operating state information relating to the operating state of the air conditioner ;
On the basis of the driving state information and a predetermined prediction model, it means for predicting the operating state of the air conditioner in the predetermined time period,
Wherein the evaluation value comfort over the predetermined period based on the predicted operating state, the evaluation value of the power rate required for the operation of the air conditioner over the predetermined period based on information of the power unit price per unit time, Means for calculating an evaluation value related to the operation of the air conditioner over the predetermined period based on
Means for calculating control information for the air conditioner for operating so that an evaluation value related to the operation of the air conditioner is an optimum value;
Program to function as.

Images