JP6539697B2 - Power saving support system and power saving support device - Google Patents

Description

  The present invention relates to a power saving support system and a power saving support apparatus.

  In recent years, a system that monitors demand power consumption of buildings such as buildings to calculate demand power (demand power), and predicts demand power from the transition situation of predetermined demand units (for example, 30 minutes unit) in the demand power. Are known (see, for example, Non-Patent Document 1).

"Power demand monitoring system", [online], Roboticsware, Inc. [search on July 12, 2013], Internet <URL: http://www.roboticsware.co.jp/demand/demand.htm>

  However, in the forecast of demand power in a system as described above, a method of finding “slope” based on the latest trend of demand power (point cloud), that is, a method of finding slope by linear single regression by the least squares method For example, the forecast of demand power on a daily basis is not performed. Therefore, in the system as described above, it is difficult for the user of the building to carry out the planning of a power saving target having a lead time or a power saving measure.

  The present invention has been made to solve the above problems, and an object thereof is to provide a power saving support system and a power saving support apparatus capable of devising a power saving target or a power saving measure based on demand power. is there.

  In order to solve the above problem, one aspect of the present invention is a data collection device for collecting power consumption of a building as time series data of power based on a power value measured by at least a measuring device that measures power, and the data And a power saving support apparatus for predicting demand power in the building based on time series data of the power collected by the collecting apparatus, the power saving support apparatus including the time series data of the power collected by the data collecting apparatus. A learning data acquisition unit for acquiring time series data of a predetermined learning range, and difference series data indicating an arithmetic mean of differences of time series data in the predetermined learning area acquired by the learning data acquisition unit; A power saving support system comprising: a prediction processing unit that predicts demand power in a predetermined prediction period based on a predetermined statistical model.

  In one embodiment of the present invention, in the power saving support system, the prediction processing unit is generated from time series data corresponding to an explanatory variable extracted from the time series data in the predetermined learning range. Demand for the predetermined prediction period based on the difference series data, dependent variable data corresponding to a dependent variable extracted from the time series data in the predetermined learning range, and the predetermined statistical model It is characterized by predicting power.

  Further, according to one aspect of the present invention, in the power saving support system described above, the predetermined learning range is a range including a plurality of second cycle periods obtained by multiplying the first cycle period by a constant multiple times; When there is a loss in time series data corresponding to the explanatory variable, interpolation data is calculated based on time series data before and after the time of loss, and the calculated interpolation data is at the time of the loss And replacing the difference series data in the case where there is a defect in the dependent variable data corresponding to the dependent variable, excluding the first cycle period including the defect from the predetermined learning range, The first period period, which is the first period period past a predetermined learning range, is equal to the first period period including the defect in the second period period, and the first period period is Characterized in that it comprises an optimization process section for adding the circumference.

  Further, according to one aspect of the present invention, in the power saving support system described above, the first cycle period is one day, the second cycle period is one week, and the power saving support device is unique in demand power. The information processing apparatus further includes a specific day information storage unit that stores specific day information indicating a specific day that is a point, and the optimization processing unit is configured to determine the predetermined learning range based on the specific day information stored by the specific day information storage unit. It is determined whether or not the specific day is included in the specific learning range, and when the specific day is included in the predetermined learning range, the specific day is excluded from the predetermined learning range, and the specific learning range is determined from the predetermined learning range The present invention is characterized in that one past day, which is the day of the week that is equal to the specific day in the one week, is added to the predetermined learning range.

  In one embodiment of the present invention, in the power saving support system, the prediction processing unit includes the specific date in the predetermined prediction period based on the specific date information stored in the specific date information storage unit. If the specific date is included in the predetermined prediction period, the specific data is collected based on the time series data of the power on the specific date in the past collected by the data collection device. It is characterized by predicting daily demand power.

  Further, according to one aspect of the present invention, in the power saving support system, the data collection apparatus stores the time series data in a collected data storage unit that stores the collected time series data, and the power saving support apparatus In the power information storage unit that acquires the time series data from the collected data storage unit for each first period period and stores the time series data as past power information, the acquired information for each first period period The measurement data acquisition unit may store the time series data, and the learning data acquisition unit may acquire time series data of the predetermined learning range from the power information storage unit.

  In one embodiment of the present invention, in the power saving support system described above, the power saving support apparatus includes a learning information adding unit that adds the explanatory variable different from time series data of the power as learning information, and the prediction process When the learning information is added, the unit predicts the power demand and evaluates whether the added learning information is statistically valid.

  Further, according to one aspect of the present invention, in the power saving support system, the prediction processing unit generates power presentation information that at least presents demand power in the predicted predicted period, and generates the generated power presentation information. And outputting to a terminal device connected to the power saving support device via a network.

  Further, according to one aspect of the present invention, in the power saving support system, the data collection device collects measurement information regarding an area including at least power consumption in an area of a building, and the power saving support device is configured to The power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person is calculated, and the estimated information on power saving based on the calculated power consumption of the device is estimated and calculated A trial calculation processing unit is provided, which outputs trial calculation presentation information including trial calculation information to a terminal device connected to the power saving support device via a network.

  Further, according to one aspect of the present invention, in the power saving support system, the trial calculation processing unit consumes the device with respect to the power target value based on a predetermined power target value and power consumption of the device. A contribution rate of power is estimated as the estimation information, and the estimation presentation information including at least the contribution rate is output to the terminal device.

  Further, according to one aspect of the present invention, in the above power saving support system, the index indicating the load felt by the person includes an average illuminance, and the estimation processing unit has the building according to the average illuminance. A lighting calculation process of calculating power consumption of a lighting device, estimating lighting information based on the calculated power consumption of the lighting device, and outputting, to the terminal device, lighting estimation information regarding lighting including the estimation information. It is characterized by including a part.

  Further, according to one aspect of the present invention, in the power saving support system described above, the lighting trial calculation processing unit is first trial calculation information that is trial calculation information regarding the lighting when the average illuminance is a first reference value; When the average illuminance is the second reference value, the second estimate information, which is estimate information regarding the illumination, is estimated, and the first estimate information calculated by estimation and the estimate presentation information regarding the illumination including the second estimate information is calculated And outputting to the terminal device.

  Further, according to one aspect of the present invention, in the power saving support system described above, the measuring device measures the power consumption of one of the plurality of lighting devices provided in the area of the building. The sensor measures the power consumption of the one unit, and the second sensor that measures the total power consumption of the lighting device provided in the area measures the total power consumption of the lighting device, and the data collection device Collects the power consumption of the one unit and the total power consumption of the lighting device as the measurement information for each area, and the lighting estimation processor processes the space condition of the area having the lighting device; The required number of the lighting devices according to the average illuminance is calculated as the estimated information based on the used power of one table and the total used power of the lighting devices, including at least the required number of the lighting devices. And outputting the estimated presenting information to the terminal device.

  Further, according to one aspect of the present invention, in the power saving support system described above, the index indicating the load felt by the person includes a discomfort index indicating an index felt by the person, which is calculated based on temperature and humidity. The estimation processing unit calculates the power consumption of the air conditioners of the building according to the discomfort index, estimates the information on air conditioning based on the calculated power consumption of the air conditioners, and calculates the estimated information And an air conditioning trial calculation processing unit that outputs, to the terminal device, the trial calculation presentation information related to the air conditioning.

  Further, according to one aspect of the present invention, in the above-described power saving support system, the measuring device measures a power used by the air conditioner and measures a temperature in an area of the building to measure the temperature in the area. The temperature is measured, and the humidity in the area is measured by a humidity sensor that measures the humidity in the area, and the data acquisition device determines the power consumption of the air conditioner, the temperature in the area, and the temperature in the area. Humidity is collected for each area as the measurement information, the air conditioning trial calculation unit calculates the discomfort index based on the temperature in the area and the humidity in the area, and the data collection device collects The power consumption of the air conditioner according to the calculated discomfort index is calculated based on the power consumption of the air conditioner in the past.

  Further, according to one aspect of the present invention, in the above power saving support system, the power saving support apparatus is configured based on the power consumption of the building measured by the measuring device before and after the power saving request for requesting cooperation of power saving. An evaluation index value regarding power saving of the building before and after the power saving request is calculated, and evaluation presentation information including an evaluation result of power saving based on the calculated evaluation index value is connected to the power saving support device via the network And an evaluation processing unit for outputting the information.

  In one embodiment of the present invention, in the power saving support system described above, the plurality of data collection devices are provided, and each of the plurality of data collection devices collects power consumption of each of the plurality of buildings, and the evaluation The processing unit, based on the power consumption of the plurality of buildings measured by the measuring device before and after the power saving request, a change in the power consumption of the buildings before and after the power saving request is between the plurality of buildings A power saving effort test unit that calculates test information for testing whether there is a significant difference as the evaluation index value and outputs the evaluation presentation information including the evaluation result based on the calculated test information to the terminal device It is characterized by

  In one embodiment of the present invention, in the power saving support system, the building has a plurality of areas, and the data collection device collects time series data of the power for each of the areas, and the evaluation process The unit ranks the power usage for each area based on the power usage for each area in the building measured by the measuring device before and after the power saving request, and ranks each before and after the power saving request The power saving order of calculating the correlation coefficient of the power used for each area as the evaluation index value based on the rank, and outputting the evaluation presentation information including the evaluation result based on the calculated correlation coefficient to the terminal device An evaluation unit is provided.

  Further, according to one aspect of the present invention, there is provided a data collection device for collecting measurement information on an area including at least power consumption in an area of a building, and power saving in the building based on the measurement information collected by the data collection device. And the power saving support apparatus, based on the measurement information, calculates the power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person, A power saving support system is provided with a trial calculation processing unit that calculates trial calculation information on power saving based on the calculated power consumption of the device.

Moreover, one aspect of the present invention relates to power saving in the building based on the power consumption of the building collected by the data collection device, and a data collection device collecting the used power of the building at least by a measuring device that measures power. And a power saving support apparatus for evaluating the power saving support apparatus, the power saving support apparatus determines the power saving request before and after the power saving request based on the power consumption of the building measured by the measuring device before and after the power saving request for requesting a power saving cooperation. The data processing system further includes an evaluation processing unit that calculates an evaluation index value related to power saving of a building, and outputs evaluation presentation information including an evaluation result of power saving based on the calculated evaluation index value, the building has a plurality of areas, and the data The collection device collects time series data of the power for each of the areas, and the evaluation processing unit uses the measuring device before and after the power saving request. The power consumption for each area is ranked based on the power consumption for each area in the building measured, and the order of power consumption before the power saving request for each area and the power saving request for each area Based on the order of the power consumption before the power saving request and the rank order of the power consumption after the power saving request, which ranks the reduction rate of the power consumption with the rank order of the power saving request and rank them before and after the power saving request, A rank correlation coefficient is calculated as the evaluation index value, and the evaluation presentation information including the evaluation result based on the calculated rank correlation coefficient is output to a terminal device connected to the power saving support device via the network. It is a power saving support system characterized by providing a power saving order evaluation part .

  Further, according to one aspect of the present invention, in the time series data of the power collected by the data collection apparatus, the power consumption of the building is collected as time series data of power based on a power value measured by at least the measuring device that measures the power. A learning data acquisition unit for acquiring time series data of a predetermined learning range, and difference series data indicating an arithmetic mean of differences of time series data in the predetermined learning area acquired by the learning data acquisition unit; And a prediction processing unit that predicts demand power in a predetermined prediction period based on a predetermined statistical model.

  Further, according to one aspect of the present invention, there is provided a power saving support for estimating power saving in a building based on the measurement information collected by a data collection apparatus for collecting measurement information regarding at least an area including power consumption in an area of the building. The apparatus calculates the power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person based on the measurement information, and estimates the power saving based on the calculated power consumption of the device According to another aspect of the present invention, there is provided a power saving support device including a trial calculation processing unit that performs trial calculation of information.

Another embodiment of the present invention is based on the power usage of the building where the data collection device collects for collecting power usage of a building having a plurality of areas by the measuring device for measuring at least the power to each of the area, the building Power saving support apparatus for performing evaluation on power saving in the house, the power saving of the building before and after the power saving request based on the power consumption of the building measured by the measuring device before and after the power saving request requesting the cooperation of the power saving An evaluation processing unit that calculates an evaluation index value related to the information and outputs evaluation presentation information including an evaluation result of power saving based on the calculated evaluation index value, the evaluation processing unit using the measuring device before and after the power saving request The power consumption for each area is ranked based on the measured power consumption for each area in the building, The consumption before the power saving request, which is ranked before and after the power saving request by ranking the order of the power consumption before the power saving request of the area and the ranking of the reduction rate of the used power after the power saving request for each area Based on the order of power and the order of reduction rate of power consumption after the power saving request, a rank correlation coefficient is calculated as the evaluation index value, and the evaluation including the evaluation result based on the calculated rank correlation coefficient It is a power saving support device characterized by outputting presentation information to a terminal device connected to the power saving support device via a network .

  According to the present invention, the power saving target or the power saving measure can be formulated based on the demand power.

It is a block diagram showing an example of the power saving support system by a 1st embodiment. It is an explanatory view explaining difference series data in this embodiment. It is explanatory drawing explaining an example of a process in case there is a defect | deletion in the data of the explanatory variable in this embodiment. It is a figure which shows an example of the measurement data of the learning range in this embodiment. It is a flow chart which shows an example of prediction processing of demand power in this embodiment. It is a flowchart which shows an example of the optimization process of the acquired measurement data in this embodiment. It is a figure which shows the example of a display in the case of displaying on the terminal device of the prediction result of the demand power in this embodiment. It is a figure which shows the example of a display in the case of displaying the prediction result of the demand power in this embodiment on a portable terminal device. It is a figure which shows the 1st graph which compared the prediction result and actual value of demand power in this embodiment. It is a 1st figure which shows the comparison with the measurement average value of demand power in this embodiment, and a prediction average value. It is a figure which shows the 2nd graph which compared the prediction result and actual value of demand power in this embodiment. It is a 2nd figure which shows the comparison with the measurement average value of demand power in this embodiment, and a prediction average value. It is a block diagram showing an example of the power saving support system by a 2nd embodiment. It is a flowchart which shows an example of the prediction process which added the explaining variable information in this embodiment, and an evaluation process. It is explanatory drawing explaining the setting information for the illumination of the area in this embodiment, and an example of the electric power measurement of illumination equipment. It is a figure which shows an example of the reference | standard of the illumination intensity in this embodiment. It is a figure which shows an example of the reference | standard of illumination intensity different from FIG. It is a figure which shows an example of the screen of the parameter setting for illumination intensity calculation in this embodiment. It is a figure which shows an example of the reference value of the maintenance rate in this embodiment. It is a figure which shows an example of the menu of lamp | ramp selection in this embodiment. It is a flowchart which shows an example of the power-saving trial calculation process of the illuminating device in this embodiment. It is a figure which shows an example of the display screen of the power-saving trial calculation result of the illuminating device in this embodiment. It is an explanatory view explaining an example of temperature measurement and humidity measurement of an area in this embodiment. It is a figure which shows an example of the reference | standard of the discomfort index in this embodiment. It is a flowchart which shows an example of the power-saving trial calculation process of the air conditioner in this embodiment. It is a figure which shows an example of the display screen of the power saving trial calculation result of the air conditioner in this embodiment. It is a figure which shows an example of the display screen of (DELTA) T and calorie | heat amount calculation result in this embodiment. It is a flowchart which shows an example of control processing of the air conditioning in this embodiment. It is a block diagram showing an example of the power saving support system by a 3rd embodiment. It is a flowchart which shows an example of the analysis process of the power-saving effort in this embodiment. It is a figure which shows an example of the display screen of the analysis result in analysis processing of the power-saving effort in this embodiment. It is a flowchart which shows an example of an analysis process of the power saving order correlation coefficient in this embodiment. It is a figure which shows an example of the display screen of the analysis result in an analysis process of the power saving order correlation coefficient in this embodiment.

Hereinafter, a power saving support system according to an embodiment of the present invention will be described with reference to the drawings.
First Embodiment
In the present embodiment, for example, a power saving support system will be described which predicts demand power of a future building (for example, demand power two days ahead) based on used power data of buildings for the past three months.

FIG. 1 is a block diagram showing an example of the power saving support system 1 according to the first embodiment.
In this figure, the power saving support system 1 includes a power saving support apparatus 100, a data collection apparatus 200, and a plurality of measuring devices 40 (40-1, 40-2,...). The power saving support system 1 also includes a plurality of terminal devices 10 (10-1, 10-2,...) Connected to the power saving support device 100 via the network N 1 and a FW (Fire Wall) device 20. The external cooperation server 300 connected with the power saving support device 100, and the portable terminal devices 30 (30-1, 30-2,...) Are provided. The mobile terminal device 30 is connected to the external cooperation server 300 via a network N2 such as a mobile carrier network or a public wireless LAN.

Here, when the measuring device 40-1, the measuring device 40-2, ... indicate any measuring device included in the power saving support system 1, or when not making a distinction, "-1", "-2" Description of "etc." is abbreviate | omitted and demonstrated as the measuring device 40. FIG.
In addition, when terminal devices 10-1, 10-2, ... indicate an arbitrary terminal device included in the power saving support system 1, or when not making a distinction, "-1", "-2", etc. The description is omitted and the terminal device 10 is described.
Moreover, when showing portable terminal devices with which the power-saving assistance system 1 is provided, or not distinguishing in particular, portable terminal devices 30-1, 30-2, ... "-1", "-2" The description of the etc. is omitted and the mobile terminal device 30 will be described.

The data collection device 200 and the measuring device 40 are provided in a building such as a building (for example, building B1 (building 1)), and the power saving support system 1 includes the data collecting device 200 and the measuring device 40 in this manner. There are multiple. The plurality of measuring devices 40 are provided in the area (for example, area A1 (area 1)), and the building has a plurality of areas including the plurality of measuring devices 40 in this manner.
Here, the area is, for example, a predetermined range such as a floor provided in a building or a room.

  The measuring device 40 is a device that measures at least power, and measures power at predetermined time intervals (for example, 30 minutes). The measuring device 40 acquires measurement data from, for example, a power sensor, a temperature sensor, and a humidity sensor, and transmits the measurement data to the data acquisition device 200. The measuring device 40 acquires and transmits measurement data using, for example, UDP (User Datagram Protocol).

The data collection device 200 is a server device that collects the power consumption of a building as time series data of power based on the power value measured by the measuring device 40 at intervals of 30 minutes. The data collection device 200 includes a collection processing unit 210 and a collected data storage unit 220.
The collection processing unit 210 is a processor including, for example, a CPU (Central Processing Unit) and the like, and controls the data collection apparatus 200 in a centralized manner. The collection processing unit 210 includes a power collection unit 211.

The power collection unit 211 acquires power values at intervals of 30 minutes from the measuring device 40, for example, and collects time series data indicating power consumption for each building based on the acquired power values and power consumption for each area Make it memorize in 220.
The collected data storage unit 220 stores collected data including time series data indicating power consumption for each building and for each area.

The power saving support device 100 is a server device that predicts future demand power in a building based on time series data of power collected by the data collection device 200. The power saving support apparatus 100 includes a processing unit 110, a storage unit 120, and a measurement data acquisition unit 130.
The storage unit 120 stores various types of information used in various types of processing in the power saving support apparatus 100. The storage unit 120 includes, for example, a power information storage unit 121, a calendar information storage unit 122, a setting information storage unit 123, and a prediction result storage unit 124.

The power information storage unit 121 stores time series data for each building and each area as past power information.
The calendar information storage unit 122 (specific date information storage unit) stores specific date information indicating a specific date (for example, a holiday, a holiday, a summer vacation, etc.) which is a specific point in the power demand. Here, the specific day information is assumed to be stored in advance by the system administrator.
The setting information storage unit 123 stores setting information used in various processes in the power saving support apparatus 100. The setting information storage unit 123 stores, for example, a power saving target value (threshold value), switching setting information of a learning period for prediction, and the like.
The prediction result storage unit 124 stores the prediction result of the future demand power predicted by the processing unit 110.

  The measurement data acquisition unit 130 acquires time-series data from the collected data storage unit 220 of the data collection device 200 every day (an example for each first period period), and uses the time-series data as past power information for power information It is stored in the storage unit 121. The measurement data acquisition unit 130 acquires time series data for one day from the collected data storage unit 220, for example, periodically (once a day) by batch processing, and uses the acquired time series data as past power information It is stored in the power information storage unit 121.

The processing unit 110 is a processor including, for example, a CPU, and executes various processes in the power saving support apparatus 100. The processing unit 110 includes a learning data acquisition unit 111, an optimization processing unit 112, and a prediction processing unit 113.
The learning data acquisition unit 111 acquires time series data of a predetermined learning range (for example, three months) among time series data of power collected by the data collection apparatus 200. Here, the predetermined learning range is a plurality of times (for example, 12 times) a second cycle period (for example, 1 week) obtained by multiplying the first cycle period (for example, 1 day) by a constant (for example, 7 times) ) Is included. That is, the predetermined learning range is a three-month period having 12 days of seven days (one week) with the first cycle period as one day. Thus, the learning data acquisition unit 111 acquires, for example, time series data of power for three months from the power information storage unit 121 as time series data of a learning range.

  The optimization processing unit 112 performs processing for optimizing time series data of the learning range (for example, the past three months) acquired by the learning data acquisition unit 111. Specifically, the optimization processing unit 112 generates difference series data indicating the arithmetic mean of the difference of the time series data in the predetermined learning range acquired by the learning data acquisition unit 111. In addition, for example, when there is a loss (lost) in the time-series data corresponding to the explanatory variable used for the prediction of the demand power, the optimization processing unit 112 interpolates based on the time-series data before and after the lost time. Data are calculated, and the calculated interpolation data is substituted for the difference series data at the missing time.

Further, for example, when there is a loss (lost) in the dependent variable data corresponding to the dependent variable used to predict the demand power, the optimization processing unit 112 excludes one day including the loss from the learning range of the past three months. , One day past the learning range of the past three months, and which is equal to the day of the week containing the deficit in one week, is added to the learning range. That is, when there is a loss (lost) in the dependent variable data corresponding to the dependent variable, the optimization processing unit 112 defines the lost portion as null (Null) and takes the form excluded from the learning range, and applies Apply the learning range to which the missing part (lost part) has been added from the learning range (for example, the past 3 months + learning target period for n days (number of missing: n)).
In addition, the optimization processing unit 112 determines whether a holiday or a public holiday (an example of a specific day) is included in the learning range of the past three months based on the specific day information (for example, holiday information) stored in the calendar information storage unit 122 It is determined whether or not the learning range includes holidays or holidays, the holiday or holiday is excluded from the learning range of the past three months, and is one day past the learning range of the past three months, Add one day of the week that is equal to the holiday or public holiday in one week to the learning range.
The detailed processing of the optimization processing unit 112 described above will be described later.

  The prediction processing unit 113 determines difference series data indicating an arithmetic mean of differences of time series data in a predetermined learning range (for example, a learning range in the past 3 months) acquired by the learning data acquisition unit 111, and predetermined statistics. Based on the model (e.g., linear multiple regression model), the future demand power in a predetermined prediction period (e.g., a period up to two days ahead) is predicted. That is, the prediction processing unit 113 determines the difference series data generated from the time series data corresponding to the explanatory variable extracted from the time series data in the learning range in the past three months, and the time in the learning range in the past three months Based on the dependent variable data corresponding to the dependent variable extracted from the series data and the predetermined statistical model, the future demand power in the period up to two days ahead is predicted. The prediction processing unit 113 causes the prediction result storage unit 124 to store predicted results up to two days ahead.

Further, the prediction processing unit 113 determines whether or not a holiday or a public holiday is included in two days which is a prediction period, based on the holiday information stored in the calendar information storage unit 122. When holiday or holiday is included in two days which is a prediction period, the prediction processing unit 113 determines the holiday or holiday based on the time series data of the power in the past holiday or holiday collected by the data collection device 200. The demand power of the forecast target date is predicted. Specifically, for example, the prediction processing unit 113 outputs the past performance data on holidays or holidays stored in the power information storage unit 121 as it is as a predicted value. Further, the prediction processing unit 113 may make the prediction result using the predetermined statistical model unstable for a week of continuous holidays such as summer vacation, winter vacation, GW (golden week) and one week before and after that. Therefore, for such a forecast target day of the week, past actual data is output as a forecast value as it is.
The details of the prediction processing by the prediction processing unit 113 will be described later.

  The prediction processing unit 113 generates power presentation information (for example, power display information) for presenting at least the user with the demand power in the predicted period up to two days ahead, and generates the generated power presentation information in the power saving support apparatus 100. And the terminal device 10 (or the portable terminal device 30) connected via the network.

The terminal device 10 is, for example, a client terminal, a client PC (personal computer) or the like, and displays the power presentation information (for example, power display information) output by the power saving support device 100 using browser software To present. Here, the power display information includes, for example, a transition graph of demand power per month, day, and hour such as a specified partial unit (building unit, area unit, floor unit), or for 2 days ahead from the day It includes forecast graphs of demand power.
The mobile terminal device 30 is, for example, a mobile terminal such as a mobile phone or a smartphone, and similarly to the terminal device 10 using browser software, power presentation information (for example, power display information) output by the power saving support device 100 Is displayed to the user.

  The terminal device 10 (or the portable terminal device 30) includes client terminals for unspecified general users, owners of buildings (for example, buildings) or client terminals for an energy manager, and clients for a system manager A terminal or the like may be included, and display information, information capable of changing settings, processing content, and the like may differ depending on each authority.

  The external cooperation server 300 is connected to the power saving support apparatus 100 via the FW device 20, receives power presentation information (for example, power display information) output from the power saving support apparatus 100, and transmits it to the portable terminal apparatus 30.

Next, the operation of the power saving support system 1 in the present embodiment will be described with reference to the drawings.
<Difference series data generation process>
First, generation processing of difference series data by the optimization processing unit 112 will be described.
FIG. 2 is an explanatory view for explaining the difference series data in the present embodiment.
In this figure, data D ₁ , D ₂ ,... Indicate, for example, time-series data of power consumption of a building collected by the data collection device 200, and data ⁻ d ₁ , ⁻ d ₂ ,. However, the superscript "-" in the text indicates the symbol attached directly above the letter) indicates the difference series data.
Differenced series data are arithmetic mean data of the data D _M and the data D _M-1 of the 30 minutes ago, the optimization processor 112 by the following equation (1), the differenced series data calculate.

The prediction processing unit 113 derives an explanatory variable based on the difference series data calculated in this manner, and uses it for prediction processing. Specifically, as shown in the following equation (2), the prediction processing unit 113 has attributes of five difference series data of the target time, the prediction target time, and 30 minutes before and after that time, and one hour before and after the time. It is defined as a value vector (representative vector). The attribute value vectors XV _{i1 to} XV _im indicate attribute value vectors derived by time series data of the past three months for one target time.

Here, the variable i is an integer of 1 to 48 indicating the measurement time of time series data for one day, and the variable m is an integer of 1 to 12 indicating up to the past 12 weeks (for 3 months).
Thus, linear trend and autocorrelation can be removed to some extent by generating difference series data.

<Interpolation processing when there is a defect in the data of explanatory variables>
Next, with reference to FIG. 3, an interpolation process when there is a defect in the data of the explanatory variable in the present embodiment will be described.
FIG. 3 is an explanatory diagram for explaining an example of processing when there is a defect in data of an explanatory variable in the present embodiment.
Some of the time-series data collected by the data collection device 200 may be lost (lost) due to a temporary trouble of the measuring device 40 or a temporary failure of the network. As shown in FIG. 3, when there is a defect in the data of the explanatory variable, the optimization processing unit 112 calculates the moving average (SMA) shown in the following equation (3) by the data for 30 minutes before and after the missing data. _M ) is calculated and applied as interpolation data of the target time.

In addition, the optimization processing unit 112 does not calculate the difference series data at the target time to which the interpolation data is applied due to the lack of data, and performs the interpolation of the moving average (SMA _M ) calculated based on the equation (3). Apply the data as it is.

<Power consumption forecasting process>
Next, prediction processing of demand power in the present embodiment will be described.
FIG. 4 is a view showing an example of measurement data of a learning range in the present embodiment.
In this figure, measurement data of the learning range in the past 3 months is one week's worth (7 days worth) of a set of data of 48 samples of measurement data D _{1 to} D ₄₈ obtained by measuring the power every 30 minutes a day Yes, and more. It is measurement data of 12 sets (for the past 12 weeks) of measurement data of this one week. The learning data acquisition unit 111 acquires measurement data of a learning range in the past three months as shown in FIG. 4. In addition, range SD1 shows measurement data of Monday from one week ago to 12 weeks ago, range SD2 shows measurement data of Tuesday from 12 weeks before 1 week ago, range SD7 shows from 1 week ago to 12 weeks Measurement data of previous Sunday is shown.

For example, when the prediction target day is Monday, the prediction processing unit 113 predicts the demand power based on the measurement data of the range SD1 (measurement data of Monday one week ago to 12 weeks ago). In this case, for example, the prediction processing unit 113 extracts measurement data (D _{1 to} D ₄₈ ) of Monday one week ago as a dependent variable, and generates difference series data of measurement data two weeks ago to 12 weeks ago as snow. We extract as surface variables and predict demand power for the next Monday (most recent Monday) based on a linear multiple regression model. Similarly, when the prediction target date is Tuesday, the prediction processing unit 113 predicts the demand power based on the measurement data in the range SD2.

The linear multiple regression model is a statistical model as follows.
Assuming that a set of m attribute value vectors shown in the above-mentioned equation (2) is a vector X _i , it can be expressed as the following equation (4), and for this vector X _i , a function f (X _i ) The value y _i to which the noise ε is added is considered as an example.

This case predicts the output of the function f (X _i ) given a set of N cases. In this case, it is assumed that the function f (X _i ) is given by the linear model shown in the following equation (5).

  Here, when the noise ε is a multivariate normal distribution, finding the parameter θ by maximum likelihood estimation is equivalent to finding it by the least square method, and the solution can be found by the following equation (6) .

The last equation of this equation (6) is a normal equation, and generally, the variable X indicates an explanatory variable and the variable y indicates a dependent variable.
The prediction processing unit 113 uses this linear multiple regression model as a dependent variable for observation values obtained in the past (for example, measurement data D _i of power at a certain time) and is considered to be related to the dependent variable The future value of demand power in units of 30 minutes is predicted using parameters obtained from combinations of vectors X _i which are variables.

Next, with reference to FIG.5 and FIG.6, the procedure of a prediction process of the demand power of the power saving support system 1 in this embodiment is demonstrated.
FIG. 5 is a flowchart showing an example of the demand power prediction process in the present embodiment.
In the process shown in this figure, the data collection device 200 collects measurement data of power usage as time series data, and the measurement data acquisition unit 130 of the power saving support device 100 acquires time series data from the collection data storage unit 220. In the following description, it is assumed that the time-series data is acquired and stored in advance in the power information storage unit 121 as past power information.

  In this figure, first, the learning data acquisition unit 111 of the power saving support apparatus 100 determines whether there is data for the past six months (step S101). That is, the learning data acquisition unit 111 refers to the power information storage unit 121 and determines whether or not the power information storage unit 121 stores time series data of the used power for the past six months or more. The learning data acquisition unit 111 advances the process to step S102 when the power information storage unit 121 stores time series data of the used power more than the past half year (step S101: YES). The learning data acquisition unit 111 advances the process to step S105 when the power information storage unit 121 stores time series data of used power less than the past six months (step S101: NO).

  Next, in step S102, the learning data acquisition unit 111 notifies the user of switching of the learning range. That is, the learning data acquisition unit 111 executes, for example, an inquiry as to whether or not to switch the learning range to the terminal device 10.

  Next, the learning data acquisition unit 111 determines whether switching of the learning range has been specified (step S103). Specifically, the learning data acquisition unit 111 determines whether or not the switching of the learning range has been specified by the user, for example, via the terminal device 10. The learning data acquisition unit 111 advances the process to step S104 when the switching of the learning range is specified (learning range specification for 6 months) (step S103: YES). The learning data acquisition unit 111 proceeds with the process to step S105 when the switching of the learning range (specification of the learning range of 6 months) is not specified (step S103: NO).

  Next, in step S104, the learning data acquisition unit 111 acquires data for six months. That is, the learning data acquisition unit 111 acquires time series data for the past six months from the power information storage unit 121 as time series data of the learning range, and advances the process to step S106.

  In addition, in step S105, the learning data acquisition unit 111 acquires data for three months. That is, the learning data acquisition unit 111 acquires time series data for the past three months from the power information storage unit 121 as time series data of the learning range.

  Next, in step S106, the prediction processing unit 113 of the power saving support device 100 determines whether the prediction target day is a continuous holiday. That is, based on holiday information (an example of unusual day information) indicating holidays, holidays, summer holidays, etc. stored in the calendar information storage unit 122, the prediction processing unit 113 is a holiday for two consecutive days based on holiday It is determined whether the If the prediction processing unit 113 determines that the prediction target day is a holiday for two consecutive days (step S106: YES), the processing proceeds to step S114. If the prediction processing unit 113 determines that the prediction target day is not a holiday for two consecutive days (step S106: NO), the processing proceeds to step S107.

  Next, in step S107, the optimization processing unit 112 of the power saving support device 100 generates difference series data. That is, the optimization processing unit 112 calculates the difference series data from the time series data of the learning range according to the above-mentioned equation (1).

  Next, the optimization processing unit 112 executes optimization processing on time-series data in the learning range (step S108). The optimization process in step S108 will be described later with reference to FIG.

  Next, the prediction processing unit 113 of the power saving support apparatus 100 determines whether the prediction target day is a holiday or a holiday (step S108). That is, based on the holiday information (one example of the specific day information) indicating holidays, holidays, summer holidays, etc. stored in the calendar information storage unit 122, the prediction processing unit 113 makes one of the prediction target days a holiday Or it is determined whether it is a public holiday. If the prediction processing unit 113 determines that one of the prediction target days is a holiday or a holiday (step S109: YES), the processing proceeds to step S110. If the prediction processing unit 113 determines that the prediction target day is not a holiday or a public holiday (step S109: NO), the process proceeds to step S111.

  Next, in step S110, the optimization processing unit 112 applies the past approximate data to the prediction target day which is a holiday or a public holiday. That is, the optimization processing unit 112 applies the past performance data stored in the power information storage unit 121 as prediction data of demand power to a prediction target day which is a holiday or a holiday.

  Next, the optimization processing unit 112 determines whether a learning range includes holidays or holidays (step S111). That is, the optimization processing unit 112 determines whether a learning range includes holidays or holidays based on the holiday information stored in the calendar information storage unit 122. If the learning range includes holidays or holidays (step S111: YES), the optimization processing unit 112 proceeds to step S112, and if the learning range does not include holidays or holidays (step S111: In the case of NO), the process proceeds to step S113.

  Next, in step S112, the optimization processing unit 112 excludes holiday or holiday data, and extends the learning range by the exclusion. That is, the optimization processing unit 112 excludes the holiday or holiday from the learning range, and adds a day that is one day past the learning range and equal to the holiday or holiday in one week to the learning range. Do.

  Next, in step S113, the prediction processing unit 113 of the power saving support device 100 performs a prediction calculation process. That is, the prediction processing unit 113 calculates the predicted value of the demand power for two days ahead based on the time series data and the difference series data of the learning range described above and the linear multiple regression model described above. In addition, in step S111, when past performance data is applied as prediction data of demand power, the prediction processing unit 113 does not perform the prediction calculation process for the prediction target date, but the prediction range of 2 days If the day includes a day that is not a holiday or a public holiday, the forecast calculation processing is performed for a day that is not a holiday or a public holiday. The prediction processing unit 113 causes the prediction result storage unit 124 to store the calculated predicted value of the required power for two days ahead.

  In addition, on the other hand, in step S114, the prediction processing unit 113 applies past actual data as prediction data. That is, the prediction processing unit 113 applies the past performance data stored in the power information storage unit 121 as prediction data of the demand power when the prediction target day is a holiday or a holiday.

  Next, the prediction processing unit 113 displays the prediction result (step S115). That is, the prediction processing unit 113 generates power presentation information (power display information) including the prediction result based on the predicted result, and outputs the power presentation information to the terminal device 10, for example. Thereby, the terminal device 10 displays the power presentation information (power display information) on the display unit (not shown), presents the prediction result to the user, and ends the processing.

Next, with reference to FIG. 6, the optimization process which is the process of step S108 of FIG. 5 will be described.
FIG. 6 is a flowchart showing an example of optimization processing of acquired measurement data in the present embodiment.
In this figure, first, the optimization processing unit 112 determines whether the dependent variable is lost (defective) (step S201). That is, the optimization processing unit 112 determines whether or not there is a defect in the dependent variable based on whether or not there is a null in the dependent variable extracted from the acquired data of the past three months. If there is a defect in the dependent variable (step S201: YES), the optimization processing unit 112 proceeds with the process to step S202. Further, when there is no defect in the dependent variable (step S201: NO), the optimization processing unit 112 advances the process to step S202.

Next, in step S202, the optimization processing unit 112 extends the learning range by the number of nulls.
Next, in step S203, the optimization processing unit 112 determines whether or not there is a loss in the explanatory variable. If there is a defect in the explanatory variable (step S203: YES), the optimization processing unit 112 proceeds with the process to step S204. In addition, when there is no defect in the explanatory variable (step S203: NO), the optimization processing unit 112 ends the process.

Next, in step S204, the optimization processing unit 112 executes moving average processing. That is, the optimization processing unit 112 calculates the moving average (SMA _M ) shown in FIG. 3 and applies it as interpolation data of the target time.

Next, with reference to FIGS. 7 and 8, an example of display of the prediction result in step S115 of FIG. 5 will be described.
FIG. 7 is a diagram showing a display example in the case of displaying on the terminal device 10 the prediction result of the demand power in the present embodiment.
In this figure, the screen G1 shows a display screen displayed by the terminal device 10. In an example shown in this figure, the prediction processing unit 113 of the power saving support apparatus 100 calculates yesterday's demand power (waveform W1), demand power on the day (waveform W2), prediction results for the next day (for 2 days) (waveform W3) And the target power value (LV1) are generated (an example of the power presentation information), and the terminal device 10 displays the display screen G1 transmitted from the power saving support device 100. The prediction processing unit 113 may have a function of displaying a power point by mouse over, as shown at time T1. Further, the prediction processing unit 113 defaults the display state including the past and the past on the day, but has a non-display function capable of collectively hiding the day and the past according to the user's option. It is also good.

Moreover, FIG. 8 is a figure which shows the example of a display in the case of displaying the prediction result of the demand power in this embodiment on the portable terminal device 30. As shown in FIG.
In this figure, the screen G2 shows a display screen displayed by the mobile terminal device 30. In an example shown in this figure, the prediction processing unit 113 of the power saving support device 100 calculates the forecast result of demand power on the day (waveform W4), demand power on the day (bar graph BG1), peak power (P1), and target power value ( The display screen G2 including the LV2) is generated, and the mobile terminal device 30 displays the display screen G2 transmitted from the power saving support device 100 via the external cooperation server 300.

Next, the accuracy of the prediction result of the required power by the power saving support system 1 of the present embodiment will be described with reference to FIGS.
FIG. 9 is a diagram showing a first graph comparing the predicted result of the demand power and the actual measurement value in the present embodiment. Moreover, FIG. 10 is a 1st figure which shows the comparison with the measurement average value of demand power in this embodiment, and a prediction average value.
In the examples shown in FIG. 9 and FIG. 10, the comparison between the prediction result of the “building A” predicted by the power saving support system 1 and the actual measurement value is shown. In the graph shown in FIG. 9, the vertical axis indicates the demand power (kW (kilowatts)), and the horizontal axis indicates the time. Further, in FIG. 9, the waveform W5 indicates the predicted value of the demand power of the "building A", and the waveform W6 indicates the actual value of the demand power in the same period as the predicted value of the "building A". Thus, the prediction result by the power saving support system 1 shows the same tendency as the actual measurement value, and the power saving support system 1 in this embodiment can predict the demand power with high accuracy.

  Moreover, the graph shown in FIG. 10 has shown the comparison with the measurement average value in "building A" shown in FIG. 9, and a prediction average value, a vertical axis shows demand power (kW), and a horizontal axis shows an item ( Measured average value and predicted average value) are shown. As shown in this figure, the average error of the demand power, which is the absolute value of the difference between the measured average value and the predicted average value, is 12.19 kW, and the error with respect to the measured average value is 0.75%. As described above, the power saving support system 1 in the present embodiment can predict the demand power with high accuracy.

Moreover, FIG. 11 is a figure which shows the 2nd graph which compared the prediction result and actual value of the demand power in this embodiment. FIG. 12 is a second diagram showing a comparison of the actual measurement average value of demand power and the prediction average value in the present embodiment.
In the examples shown in FIGS. 11 and 12, the power saving support system 1 compares the predicted result and the actual measurement value predicted for the “building B” different from the “building A” described above. In addition, "Building A" and "Building B" are, for example, buildings having different conditions such as the scale and the heat retaining state such as the exterior. In the graph shown in FIG. 11, the vertical axis indicates the demand power (kW) and the horizontal axis indicates the time. Further, in FIG. 11, the waveform W7 indicates the predicted value of the demand power of the "building B", and the waveform W8 indicates the actual value of the demand power in the same period as the predicted value of the "building B".

Moreover, the graph shown in FIG. 12 has shown the comparison with the measurement average value in "Building B" shown in FIG. 11, and a prediction average value, a vertical axis shows demand power (kW), and a horizontal axis shows an item ( Measured average value and predicted average value) are shown. As shown in this figure, the average error of the demand power, which is the absolute value of the difference between the measured average value and the predicted average value, is 0.655 kW, and the error with respect to the measured average value is 0.88%.
As described above, the power saving support system 1 according to the present embodiment can predict the demand power with high accuracy even in a “building B” different from the “building A”.

  As described above, the power saving support system 1 according to the present embodiment includes the data collection device 200 and the power saving support device 100. The data collection device 200 collects the power consumption of the building as time-series data of power based on power values measured at predetermined time intervals by at least the measuring device 40 that measures power. The power saving support apparatus 100 predicts future demand power in the building based on time series data of past power collected by the data collection apparatus 200. Furthermore, the power saving support apparatus 100 includes a learning data acquisition unit 111 and a prediction processing unit 113. The learning data acquisition unit 111 acquires time series data of a predetermined learning range (for example, the past three months) among time series data of the past power collected by the data collection apparatus 200. The prediction processing unit 113 determines the difference series data indicating the arithmetic mean of the difference of time series data in the predetermined learning range acquired by the learning data acquisition unit 111, and a predetermined statistical model (for example, a linear multiple regression model). Predict future demand power in a predetermined forecast period (for example, two days ahead). The prediction processing unit 113 extracts the difference series data generated from the time series data corresponding to the explanatory variable extracted from the time series data in the predetermined learning range and the time series data in the predetermined learning range The future demand power in a predetermined prediction period is predicted based on the dependent variable data corresponding to the selected dependent variable and the predetermined statistical model.

  In this way, linear trend and autocorrelation can be removed by converting time series data into difference series data and using it for a predetermined statistical model, so the power saving support system 1 in the present embodiment can reduce the demand power. It can be predicted with high accuracy. Also, by this, the power saving support system 1 in the present embodiment can predict the future of the demand power ahead in a long period of time, for example, on a daily basis. Therefore, the power saving support system 1 according to the present embodiment can implement planning of a power saving target having a lead time or a power saving measure by the user of the building. Therefore, the power saving support system 1 according to the present embodiment can appropriately plan the power saving target or the power saving measure based on the demand power (for example, the predicted demand power).

  Further, in the present embodiment, the predetermined learning range (for example, the past three months) mentioned above includes a plurality of second cycle periods (for example, one week) obtained by multiplying the first cycle period (for example, one day) by a constant. The power saving support device 100 includes an optimization processing unit 112. When there is a loss in the time-series data corresponding to the explanatory variable, the optimization processing unit 112 calculates interpolation data based on time-series data before and after the lost time, and the calculated interpolation data is lost. Replace the difference series data at the current time. Then, when there is a loss in the dependent variable data corresponding to the dependent variable, the optimization processing unit 112 excludes the first cycle period including the loss from the predetermined learning range, and performs the first past of the predetermined learning range. A first period period, which is equal to the first period period including a defect in the second period period, is added to the predetermined learning range. That is, when there is a defect in the dependent variable data corresponding to the dependent variable used for the prediction of demand power, the optimization processing unit 112 excludes one day including the defect from the learning range of the past three months, Add one day to the learning range that is one day past the learning range and equal to the day of the week that contains the defect in one week.

  As a result, even if some data are lost due to, for example, a temporary trouble of the measuring device 40 or a temporary failure of the network, the robustness (fault tolerance) can be secured. The power saving support system 1 according to the embodiment can predict the demand power with high accuracy.

Further, in the present embodiment, the first cycle period described above is one day, the second cycle period described above is one week, and the power saving support apparatus 100 determines a singular date (for example, a singular point in the demand power) Calendar information storage unit 122 (an example of a specific day information storage unit) that stores special day information (for example, holiday information) indicating a holiday, etc.) is provided. Then, the optimization processing unit 112 determines whether or not a specific date is included in a predetermined learning range (for example, the past three months) based on the specific date information stored in the calendar information storage unit 122. When the learning range includes a specific day, the specific day is excluded from the predetermined learning range, and is one day past the predetermined learning range, which is the day of the week equal to the specific day in one week. Is added to the predetermined learning range.
Thus, the power saving support system 1 according to the present embodiment can predict the demand power with high accuracy even if a specific learning range includes, for example, a special day such as a holiday.

Further, in the present embodiment, the prediction processing unit 113 determines the specific date (for example, the previous two days) based on the specific date information (for example, holiday information) stored in the calendar information storage unit 122 (for example, two days ahead). , Holiday) is determined, and when a specific date is included in the predetermined prediction period, based on the time series data of power on the previous specific date collected by the data collection device 200, Forecast demand power on a singular day.
Thus, the power saving support system 1 according to the present embodiment can appropriately forecast the demand power even in the case where the prediction period includes a special day such as a holiday.

Further, in the present embodiment, the data collection device 200 stores the time series data in the collected data storage unit 220 that stores the collected time series data. The power saving support apparatus 100 acquires time series data from the collected data storage unit 220 every first cycle period (for example, one day), and stores the time series data as past power information in the power information storage unit 121, The measurement data acquisition unit 130 is configured to store the acquired time-series data for each first period period. Then, the learning data acquisition unit 111 acquires time series data of a predetermined learning range from the power information storage unit 121.
As a result, the power saving support device 100 stores the time series data collected by the data collection device 200 in the power information storage unit 121 for each first cycle period, and collects the demand power of the building by distributed processing. The power saving support system 1 according to the present embodiment can appropriately collect the demand power of the building while reducing the amount of information processing and the load of the network required for the collection process of the demand power of the building.

Further, in the present embodiment, the prediction processing unit 113 generates power presentation information (for example, power display information) that at least presents demand power in a predicted predetermined prediction period (for example, the previous two days), and generates the generated power The presentation information is output to the terminal device 10 (or the portable terminal device 30) connected to the power saving support device 100 via the network.
Thereby, the power saving support system 1 in the present embodiment can present the prediction result of the demand power to the user via the terminal device 10 (or the mobile terminal device 30). Therefore, the user can appropriately plan the power saving target or the power saving measure.
For example, when the user is an unspecified general user, by grasping the power consumption tendency of two days ahead in the prediction, the power saving awareness in daily life is improved, and the summer power saving measures (request) etc. are addressed. It can be used as a single indicator. As described above, the power saving support system 1 according to the present embodiment can support an effective power saving measure such as an area where a general general user of a building is in charge.

Also, for example, when the user is the energy manager of the building, it is possible to grasp the trend of the power saving measures in summer and the maximum demand power, and execute or execute the power saving plan as needed with reference to the predicted value. The effect verification can be performed by comparing the predicted value and the actual value later.
Further, for example, when the user is a system administrator, it is possible to appropriately confirm the predicted result of the power demand, and to investigate the cause and change the setting with respect to the predicted result which seems to be extremely unstable.

Second Embodiment
Next, a power saving support system according to a second embodiment will be described with reference to the drawings.
In the present embodiment, in addition to the prediction process in the first embodiment, for example, a process of evaluating a predicted value by adding a new explanatory variable, and a process of performing a trial calculation regarding power saving according to average illuminance or discomfort index And the process which displays the air temperature of an area by cal (calories) and J (joules) is added.

FIG. 13 is a block diagram showing an example of the power saving support system 1a according to the second embodiment.
In this figure, the power saving support system 1a includes a power saving support device 100a, a data collection device 200a, a plurality of measuring devices 40 (40-1, 40-2,...), And a plurality of air conditioners 50 (50-1, 50-2, ...). In addition, the power saving support system 1 a includes a plurality of terminal devices 10 (10-1, 10-2,...) Connected to the power saving support device 100 a via the network N 1 and a FW (Fire Wall) device 20. The external cooperation server 300 connected with the power saving support device 100a, and the portable terminal devices 30 (30-1, 30-2,...) Are provided.

Here, when the air conditioner 50-1, air conditioner 50-2, ... indicates any air conditioner provided in the power saving support system 1a, or when not particularly distinguished, "-1", "-2" Description of "etc." is abbreviate | omitted and demonstrated as the air conditioner 50. FIG.
Further, the data collection device 200a, the measuring device 40, and the air conditioner 50 are provided in a building such as a building (for example, building B1 (building 1)), and the power saving support system 1a is thus the data collection device 200a. , And a plurality of buildings provided with the measuring devices 40 and the air conditioners 50. Further, the plurality of measuring devices 40 are provided in the area (for example, area A1 (area 1)), and the building has a plurality of areas including the plurality of measuring devices 40 and the plurality of air conditioners 50 in this manner. ing.

  In this figure, the same components as those shown in FIG. 1 are denoted by the same reference numerals and the description thereof will be omitted. Hereinafter, a configuration different from FIG. 1 will be described.

  The air conditioner 50 is, for example, a device such as an air conditioner that adjusts the temperature and humidity in the area. In response to a request from the data collection device 200a, the air conditioner 50 receives the inside of the air conditioner 50 via, for example, an AGCU (air conditioner group control unit) unit 51 (see FIG. 23) via a BACnet (Building Automation and Control Networking protocol). Information (air conditioning information) is transmitted to the data collection device 200a.

  The data collection device 200 a includes a collection processing unit 210 a and a collected data storage unit 220. Further, the collection processing unit 210 a includes a power collection unit 211 a, an air conditioning information collection unit 212, and an air conditioning control unit 213.

The power collection unit 211a has the same function as that of the power collection unit 211 shown in FIG. 1, and measures the power of a lighting device described later.
For example, the air conditioning information collection unit 212 acquires air conditioning information from the air conditioner 50 at an interval of 30 minutes, and stores the acquired air conditioning information in the collected data storage unit 220. Here, the air conditioning information includes, for example, the set temperature of the air conditioner 50, the set humidity, the setting mode, the power used, and the like.
The air conditioning control unit 213 transmits the set temperature, the set humidity, the setting mode, the power used, and the like of the air conditioner 50 to the air conditioner 50 to control the air conditioner 50. The setting mode includes three basic control modes: "high temperature mode (low setting humidity)", "low temperature mode (high setting humidity)", and "balance operation mode".

The power saving support device 100 a includes a processing unit 110 a, a storage unit 120 a, and a measurement data acquisition unit 130.
The storage unit 120 a includes, for example, a power information storage unit 121, a calendar information storage unit 122, a setting information storage unit 123, a prediction result storage unit 124, and an air conditioning information storage unit 125.
The air conditioning information storage unit 125 stores setting information of the air conditioner 50 collected by the data collection device 200a.

  The measurement data acquisition unit 130 in this embodiment acquires the setting information of the air conditioner 50 collected by the data collection device 200a from the collected data storage unit 220, in addition to the same functions as in the first embodiment. The set information is stored in the air conditioning information storage unit 125.

  The processing unit 110 a includes a learning data acquisition unit 111, an optimization processing unit 112, a prediction processing unit 113, a learning information adding unit 114, and a trial calculation processing unit 140. The trial calculation processing unit 140 further includes a lighting calculation processing unit 141 and an air conditioning calculation processing unit 142.

The learning information adding unit 114 adds an explanatory variable different from the power time-series data as learning information. An explanatory variable to be added is, for example, weather information, etc., which can be arbitrarily set by the system administrator.
Note that, when the learning information is added, the prediction processing unit 113 in the present embodiment predicts future demand power with an explanatory variable including the added explanatory variable, and the added learning information is statistically effective. Evaluate whether there is any.

The trial calculation processing unit 140 causes the data collection device 200 a to calculate the power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person based on the measurement information on the area including at least the used power. The trial calculation processing unit 140 performs trial calculation of trial calculation information on power saving based on the calculated power consumption of the device, and performs trial calculation presentation information including the trial calculation information to the terminal device 10 connected to the power saving support device 100 a via the network. Output.
Here, the measurement information includes, for example, the used power of one of the lighting devices 60 (see FIG. 15) described later, the total used power of the lighting devices 60, the used power of the air conditioner 50, the temperature in the area, and the area It is the humidity inside. Moreover, the index which shows the load which a person feels is the average illumination intensity in illumination, or the discomfort index in air conditioning etc., for example. The device related to the load felt by a person is the lighting device 60 or the air conditioner 50.

Further, the trial calculation processing unit 140 estimates the contribution ratio of the power consumption of the device to the power target value as the trial calculation information based on the predetermined power target value and the power consumption of the device, and includes at least the contribution ratio The presentation information (for example, estimated display information) is output to the terminal device 10.
Further, the trial calculation processing unit 140 includes a lighting trial calculation processing unit 141 and an air conditioning trial calculation processing unit 142.

The lighting trial calculation unit 141 calculates the power consumption of the lighting device 60 of the building according to the average illuminance, estimates the lighting information based on the calculated power consumption of the lighting device 60, and calculates the estimated information. It outputs to the terminal device 10 estimated presentation information related to the lighting included.
The estimated information here includes the required number of the lighting devices 60 (the number of devices, the required number of lights, etc.), the contribution ratio of the power consumption of the lighting device 60 to the power target value, and the like.

In addition, the lighting estimation processor 141 calculates the required number and contribution ratio (first estimation information) when the average illuminance is the JIS standard “JIS Z9110-1979” (first reference value), and the average illuminance indicates occupational safety and health Calculate the required number and contribution rate (second estimate information) in the case of Article 10 (second standard value) of Office Hygiene Standards Regulations based on the provisions of the law. The lighting trial calculation processing unit 141 outputs, to the terminal device 10, trial presentation information on lighting including the first trial calculation information and the second trial calculation information.
The detailed processing of the lighting estimation processing unit 141 will be described later.

The air conditioning trial calculation unit 142 calculates the power consumption of the air conditioner 50 of the building according to the discomfort index, estimates the air conditioning information based on the calculated power consumption of the air conditioner 50, and calculates the estimated information It outputs to the terminal device 10 estimated presentation information regarding air conditioning including.
The estimated information here includes the power reduction rate of the air conditioner 50, the contribution rate of the power consumption of the air conditioner 50 to the power target value, and the like.
The detailed processing of the air conditioning trial calculation processing unit 142 will be described later.

Next, the operation of the power saving support system 1a in the present embodiment will be described with reference to the drawings.
The prediction process of the power saving support system 1a in the present embodiment is the same as that in the first embodiment, and thus the description thereof is omitted here.

<Additional processing of explanatory variable>
First, with reference to FIG. 14, a process of adding an explanatory variable by the learning information adding unit 114 and the prediction processing unit 113 will be described.
FIG. 14 is a flowchart showing an example of prediction processing and evaluation processing to which explanatory variable information is added in the present embodiment.
In this figure, first, the learning information adding unit 114 of the power saving support device 100a executes an explanatory variable adding process (step S301). For example, the learning information adding unit 114 adds an explanatory variable (for example, weather data or the like) specified by the system administrator as learning information via the terminal device 10. Also, the learning information adding unit 114 sets an attribute value vector for the added learning information based on a designation from the system administrator.

The subsequent processes from step S302 to step S316 are the same as the processes from step S101 to step S115 shown in FIG. 5, and thus the description thereof is omitted here.
Next, in step S317, the prediction processing unit 113 evaluates the cause variable. The prediction processing unit 113 calculates, for example, an evaluation index of a causal variable such as a multiple correlation coefficient, a determination coefficient, a correction determination coefficient, and the like by using a multiple regression analysis method.

  Next, the prediction processing unit 113 determines whether the added explanatory variable is a cause variable (step S318). That is, the prediction processing unit 113 determines whether the added explanatory variable is the cause variable based on the evaluation index of the cause variable described above. If the prediction processing unit 113 determines that the added explanatory variable is the cause variable (step S318: YES), the process proceeds to step S319. If the prediction processing unit 113 determines that the added explanatory variable is not the cause variable (step S318: NO), the process proceeds to step S320.

  Next, in step S319, the prediction processing unit 113 displays the application of the added explanatory variable. That is, the prediction processing unit 113 generates presentation information (display information) for displaying application of the added explanatory variable, outputs the generated presentation information (display information) to the terminal device 10, and ends the process.

  In addition, in step S320, the prediction processing unit 113 displays the exclusion of the added explanatory variable. That is, the prediction processing unit 113 generates presentation information (display information) that displays that the added explanatory variable is excluded, outputs the generated presentation information (display information) to the terminal device 10, and ends the processing.

<Power saving trial calculation processing about lighting>
Next, a trial calculation process regarding lighting in the present embodiment will be described with reference to the drawings.
FIG. 15 is an explanatory view for explaining an example of setting information for illumination of an area and power measurement of the illumination device 60 in the present embodiment.
In the drawing, an area A1 indicates a room such as an office having a plurality of lighting devices 60 (60-1, 60-2, 60-3,...), For example. In the area A1, the length XL indicates the area size in the frontage direction, and the length YL indicates the area size in the depth direction. The length XD indicates the distance between the lighting devices 60 in the frontage direction, and the length YD indicates the distance between the lighting devices 60 in the depth direction. The length XL, the length YL, the length XD, and the length YD are set as space conditions by the user on a setting screen described later.

The area A1 also includes power sensors (S1, S2) and a distribution board 42.
The power sensor S1 (first sensor) is disposed, for example, between the lighting device 60-1 and the lighting device 60-2, and measures the power consumption of one lighting device 60 (60-1).
The power sensor S2 (second sensor) is disposed, for example, between the distribution board 42 and the lighting device 60, and measures the total power usage of the lighting device 60.
The measuring device 40 is connected to the power sensors (S1, S2) via the center GW (gateway) unit 41, and the measuring device 40 uses the power sensor S1 for the power consumption of one lighting device 60. While measuring the total use power of the lighting apparatus 60 in the area by the power sensor S2.

  In this case, the data collection device 200a collects, for each area, the power consumption of one lighting device 60 and the total power consumption of the lighting device 60 in the area as measurement information. Then, the lighting estimation processing unit 141 determines the lighting equipment 60 according to the average illuminance based on the space conditions of the area having the lighting equipment 60, the used power of one unit, and the total used power of the lighting equipment 60 in the area. Calculate the required number of as trial calculation information. The lighting estimation processing unit 141 outputs, to the terminal device 10, estimation presentation information including at least the required number of lighting devices 60.

Next, a method of calculating the average illuminance and the estimated information (the required number of lighting devices 60, the contribution ratio of the power consumption of the lighting devices 60 to the power target value) will be described.
The following equation (7) shows a calculation model of the average illuminance.

Here, the variable Lx indicates the average illuminance or the required illuminance, and the variable A indicates the floor area (m ² ) (= length in the direction of the frontage XL (m) × length in the direction of the depth YL (m)) ing. Further, a variable lm indicates the luminous flux of the lamp (selected by the list in FIG. 20 described later). The variable LMPn indicates the number of lamps per lighting device 60. The variable N indicates the number of lighting devices 60, the variable U indicates the lighting rate calculated based on the room index, and the variable M indicates the maintenance rate of the lighting devices 60.
The lighting estimation processing unit 141 calculates the room index for calculating the lighting rate by the following equation (8).

Here, “0.85” is the height from the light source to the visual work surface, which is defined as 85 cm above the floor, and is calculated by subtracting it from the height HC of the ceiling surface.
The lighting estimation processing unit 141 calculates the power consumption of a single lamp according to the following equation (9).

Here, the variable Np indicates the power consumption of a single lamp, and the variable S1 indicates the power consumption per lighting device 60.
Next, the lighting estimation processing unit 141 calculates the required number of lights and calculates the power consumption for the required number of lights by the following equation (10).

Here, the variable P _LM indicates the power consumption at the required number of lights.
Next, the lighting estimation processing unit 141 calculates the contribution rate to the power target value (power saving target value) according to the following equation (11).

Here, the variable P _total indicates the current power consumption of the entire area, and the variable P _eco indicates the power target value (power saving target value).
Further, the lighting estimation processing unit 141 specifically calculates the required number of lamps to be turned off according to the following equation (12).

In the present embodiment, the lighting estimation processing unit 141 calculates the required number of lights in the first step and the second step, and causes the terminal device 10 to display the required number of lamps to be turned off and the contribution rate specifically. . Here, in the first step, the lighting trial calculation unit 141 calculates the necessary number and contribution rate (first trial calculation information) when the average illuminance is the JIS standard “JIS Z9110-1979” (first reference value). Do. In the second step, the lighting estimation processor 141 calculates the required number and contribution ratio in the case where the average illuminance is Article 10 (second standard value) of the office health standard rules based on the provisions of the Industrial Safety and Health Law ( Calculate second trial calculation information).
FIG. 16 shows the JIS standard “JIS Z9110-1979”, and FIG. 17 shows the above-mentioned Article 10 of the office hygiene standard rules.

FIG. 18 is a diagram showing an example of a parameter setting screen for illuminance calculation in the present embodiment.
The lighting estimation processing unit 141 causes the terminal device 10 to display a setting screen G3 as shown in this figure, and the user sets various parameters. The lighting trial calculation unit 141 acquires setting information set by the user from the terminal device 10, and stores the setting information in the setting information storage unit 123, for example.
When the “reference value” button BT1 is clicked on the setting screen G3 shown in FIG. 18, the lighting estimation processor 141 causes the terminal device 10 to display the setting screen as shown in FIG. Set the maintenance rate to In addition, when the item “lamp selection” (button BT2) is clicked, the lighting estimation processing unit 141 causes the terminal device 10 to display a setting screen as shown in FIG. 20, and sets the lamp selection to the user. Let

Next, with reference to FIG. 21, the procedure of the power saving trial calculation process regarding the illumination in this embodiment will be described.
FIG. 21 is a flowchart showing an example of power saving trial calculation processing of the lighting apparatus 60 in the present embodiment.
In this figure, first, the lighting estimation processing unit 141 executes parameter setting for proof (step S401). That is, the lighting trial calculation processing unit 141 causes the terminal device 10 to display the setting screen G3 as shown in FIG. 18, and allows the user to set the certification parameters.

  Next, the lighting estimation processing unit 141 acquires measurement data (step S402). That is, the lighting estimation processing unit 141 acquires the used power of one of the lighting devices 60 collected by the data collection device 200 a and the total used power of the lighting devices 60 in the area.

  Next, the lighting estimation processing unit 141 determines whether or not arbitrary calculation is performed (step S403). For example, the lighting estimation processing unit 141 determines whether or not an arbitrary calculation has been designated by the user. The lighting trial calculation processing unit 141 causes the process to proceed to step S406 when arbitrary calculation is performed (step S403: YES). In addition, if the lighting estimation processing unit 141 does not perform any calculation (step S403: NO), the processing proceeds to step S404 or step S405.

  In step S404, the lighting estimation processing unit 141 executes optimal calculation (estimation processing) as the first step. That is, the lighting calculation processing unit 141 is, for example, 750 lx (lux) corresponding to “office b, officer's room, meeting room, electronic system three rooms” which is JIS standard “JIS Z9110-1979” shown in FIG. Calculate the required number of lamps to be turned off and the contribution rate.

  In addition, in step S405, the lighting estimation processing unit 141 executes optimal calculation (estimation processing) as a second step. That is, for example, the lighting estimation processing unit 141 is configured to turn off the light at 350 lx corresponding to "precise work" which is Article 10 of the Office Health Standard Rules based on the provisions of the Industrial Safety and Health Law shown in FIG. Calculate the required number and contribution rate.

In step S406, the lighting estimation processing unit 141 acquires the average illuminance specified by the user on the setting screen G3 as shown in FIG.
Next, the lighting trial calculation processing unit 141 executes a trial calculation process according to the specified average illuminance (step S407). That is, the lighting estimation processing unit 141 estimates the required number of lights, the number of devices, the contribution rate, and the like according to the average illuminance.

  Next, the lighting estimation processing unit 141 displays the estimation result (step S408). That is, the lighting trial calculation processing unit 141 generates trial calculation presentation information (for example, trial calculation display information) including the trial calculation result, and outputs it to the terminal device 10. Thereby, the terminal device 10 displays a display screen G4 as shown in FIG. 22 which is estimation presentation information, and presents the estimation result to the user.

  Next, the lighting estimation processing unit 141 determines whether or not the analysis is completed (step S409). That is, the lighting trial calculation processing unit 141 determines whether or not the end of the trial calculation processing related to the lighting is designated by the user. The lighting trial calculation processing unit 141 ends the process when the end of the trial calculation process is specified (step S409: YES), and when the end of the trial calculation process is specified (step S409: NO), the process is performed to step S403. Return and repeat trial calculation process.

FIG. 22 is a view showing an example of the display screen of the power saving estimation result of the lighting device 60 in the present embodiment.
In this figure, the display screen G4 shows the estimation result by the illumination estimation processing unit 141. As shown on the display screen G4, the lighting estimation processing unit 141 writes and displays the estimation result of the first step and the estimation result of the second step described above. This allows the user to compare the estimated results. Further, as shown on the display screen G4, the lighting estimation processing unit 141 executes estimation processing according to an arbitrary average illuminance specified by clicking on the “calculation” button BT3 and displays the estimation result. As a result, the user can estimate a power saving plan in consideration of work efficiency and comfort of the worker based on the average illuminance.

<Power saving trial calculation processing about air conditioning>
Next, a trial calculation process regarding air conditioning in the present embodiment will be described with reference to the drawings.
FIG. 23 is an explanatory diagram for explaining an example of temperature measurement and humidity measurement of an area in the present embodiment.
In this figure, the area A1 is, for example, an air conditioner 50 having a plurality of temperature sensors 70 (70-1 to 70-6), a plurality of humidity sensors 80 (80-1 and 80-2), and an AGCU unit 51. Is equipped. The AGCU unit 51 can control the air conditioner 50 by measuring the power used by the air conditioner 50 and changing the setting of the air conditioner 50 such as changing the mode.

The temperature sensor 70 measures the temperature in the area, and the humidity sensor 80 measures the humidity in the area. The area temperature applies the average temperature of the temperatures measured by the plurality of temperature sensors 70, and the area humidity applies the average humidity of the humidity measured by the plurality of humidity sensors 80.
Also, the measuring device 40 is connected to the plurality of temperature sensors 70 and the plurality of humidity sensors 80 via the center GW (gateway) unit 41, and the temperature device 70 measures the temperature in the area (average In addition to measuring the temperature, the humidity sensor 80 measures the humidity in the area. The measuring device 40 may include the AGCU unit 51, and the measuring device 40 may measure the power used by the air conditioner 50.

  In this case, the data collection device 200a collects, for each area, the power used by the air conditioner 50, the temperature in the area, and the humidity in the area as measurement information. Then, the air conditioning trial calculation processing unit 142 calculates the discomfort index based on the temperature in the area and the humidity in the area, and calculates it based on the power consumption of the air conditioner 50 in the past collected by the data collection device 200a. The power consumption of the air conditioner 50 according to the discomfort index is calculated. The air conditioning trial calculation processing unit 142 uses the power reduction rate of the air conditioner 50 and the contribution rate of the power consumption of the air conditioner 50 to the power target value as trial calculation information based on the power consumption of the air conditioner 50 according to the calculated discomfort index. calculate. The air conditioning trial calculation processing unit 142 generates trial calculation presentation information including the power reduction rate of the air conditioner 50 and the contribution ratio of the power consumption of the air conditioner 50, and outputs the generated trial presentation information to the terminal device 10.

Next, a method of calculating the discomfort index and estimated information (the power reduction rate of the air conditioner 50, the contribution rate of the power consumption of the air conditioner 50 to the power target value) will be described.
The discomfort index (DI) is an index serving as a measure of comfort, and the air conditioning trial calculation processing unit 142 calculates the following equation (13).

Here, variable T indicates dry-bulb temperature (° C.), and variable H indicates humidity (%). For example, when the temperature is 38.5 ° C. and the humidity is 18%, the discomfort index is calculated as “78”.
The relationship between the discomfort index and the sensible temperature is as shown in FIG. For example, in Japanese, about 9% of people have discomfort when the discomfort index is "75", and about 65% of people have discomfort when the discomfort index is "77", the discomfort index It is said that about 93% of people are uncomfortable when "85" is.
The air conditioning trial calculation processing unit 142 calculates the discomfort index based on the information on the temperature and humidity of the area, and from the relationship as shown in FIG. 24, the threshold for setting the lower limit of the discomfort index to “59” and the upper limit to “76”. Is the default setting. For example, in the case of summer, the air conditioning trial calculation processing unit 142 sets the optimum air conditioner setting for “76” as the upper limit in response to the power saving target value set in response to the demand suppression request (power saving request) from the electric power company. Present to the user (for example, the energy manager).

Next, with reference to FIG. 25, the procedure of the power saving trial calculation process regarding the air conditioning in the present embodiment will be described.
FIG. 25 is a flowchart showing an example of power saving trial calculation processing of the air conditioner 50 in the present embodiment.
In this figure, first, the air conditioning trial calculation processing unit 142 performs setting of parameters for air conditioning (step S501). That is, the air conditioning trial calculation processing unit 142 causes the terminal device 10 to display a setting screen for air conditioning, and allows the user to set parameters for air conditioning. Here, the parameters for air conditioning include, for example, space conditions such as the size in the opening direction of the area, the size in the depth direction, and the size in the ceiling height direction.

  Next, the air conditioning trial calculation processing unit 142 acquires measurement data (step S502). That is, the air conditioning trial calculation processing unit 142 acquires the power used by the air conditioner 50 collected by the data collection device 200a, the temperature in the area (average temperature), and the humidity in the area.

  Next, the air conditioning trial calculation processing unit 142 determines whether or not arbitrary calculation is performed (step S503). For example, the air-conditioning trial calculation processing unit 142 determines whether or not the user has specified an arbitrary calculation. If the air conditioning trial calculation processing unit 142 performs arbitrary calculation (step S503: YES), the process proceeds to step S508. Further, if the air conditioning trial calculation processing unit 142 does not perform arbitrary calculation (step S503: NO), the process proceeds to step S504.

  Next, in step S504, the air conditioning trial calculation processing unit 142 acquires calendar information. Here, the calendar information is information for determining whether it is summer or winter, and the air conditioning trial calculation processing unit 142 may use the holiday information stored in the calendar information storage unit 122.

  Next, the air conditioning trial calculation processing unit 142 determines whether the season is summer (step S505). That is, based on the calendar information, the air conditioning trial calculation processing unit 142 determines whether the season is summer. If the air conditioning trial calculation processing unit 142 determines that the season is summer (step S505: YES), the process proceeds to step S506. If the air conditioning trial calculation processing unit 142 determines that the season is not summer (step S505: NO), the process proceeds to step S507.

  Next, in step S506, the air conditioning trial calculation processing unit 142 executes limit value calculation (estimate calculation process) of the summer discomfort index. For example, the air conditioning trial calculation processing unit 142 sets the temperature 25 ° C. to 27 ° C. and the humidity 50% to 60% to estimate the power reduction rate of the air conditioner 50 and the contribution rate of the power consumption of the air conditioner 50.

  In addition, in step S507, the air conditioning trial calculation processing unit 142 executes limit value calculation (estimate calculation process) of the winter discomfort index. For example, the air conditioning trial calculation processing unit 142 sets the temperature to 18 ° C. to 20 ° C. and the humidity to 40% to 50%, and estimates the power reduction rate of the air conditioner 50 and the contribution rate of the power consumption of the air conditioner 50.

In addition, in step S508, the air conditioning trial calculation processing unit 142 acquires the temperature and the humidity designated by the user. For example, the air conditioning trial calculation processing unit 142 estimates the obtained temperature and humidity from the power reduction rate of the air conditioner 50 and the contribution rate of the power consumption of the air conditioner 50.
Next, the lighting trial calculation processing unit 141 executes a trial calculation process according to the specified average illuminance (step S509). That is, the air conditioning trial calculation processing unit 142 calculates the discomfort index based on the designated temperature and humidity, and the power reduction rate of the air conditioning device 50, the contribution rate of the power consumption of the air conditioning device 50, etc. Make an estimate.

  Next, the air conditioning trial calculation processing unit 142 displays the trial calculation result (step S510). That is, the air conditioning trial calculation processing unit 142 generates trial calculation presentation information (for example, trial calculation display information) including the trial calculation result, and outputs it to the terminal device 10. Thereby, the terminal device 10 displays a display screen G5 as shown in FIG. 26 which is estimation presentation information, and presents the estimation result to the user.

  Next, the air conditioning trial calculation processing unit 142 determines whether the analysis is finished (step S511). That is, the air conditioning trial calculation processing unit 142 determines whether or not the end of the trial calculation process related to the air conditioning has been designated by the user. The air conditioning trial calculation processing unit 142 ends the process when the end of the trial calculation process is specified (step S511: YES), and when the end of the trial calculation process is specified (step S511: NO), the process proceeds to step S503. Return and repeat trial calculation process.

FIG. 26 is a view showing an example of a display screen of the power saving estimation result of the air conditioner 50 in the present embodiment.
In this figure, the display screen G5 shows the estimation result by the air conditioning estimation processor 142. As shown in the display screen G5, the air conditioning trial calculation processing unit 142 displays the power reduction rate and the contribution rate according to the discomfort index as a trial calculation result. Further, as shown on the display screen G4, the lighting trial calculation processing unit 141 executes the trial calculation processing according to the specified arbitrary temperature and humidity by clicking the “calculation” button BT4, and displays the result of the trial calculation Let This allows the user to estimate a power saving plan taking into account the worker's work efficiency and comfort according to the discomfort index.

Next, a process of displaying the temperature change amount ΔT (the change amount of the air temperature) for each area in the present embodiment will be described with reference to FIG.
By the way, temperature is by far the most important factor to be managed in air, and it is "heat" that influences temperature. "Heat" generally refers to "warm heat", but even when it is cold, "heat" is present ("cold heat"). "Heat" is one of the transfer modes of energy, and the amount of energy transferred through the form of "heat" is called "heat quantity", and "heat" is always from a hot object to a cold object. Moving. There are two units of calorific value, cal (calorie) and J (joule), and in this embodiment, both are indicated, and energy is used as a management element and basic information for various physical and statistical analyses. Present to management.

FIG. 27 is a diagram showing an example of a display screen of the ΔT · heat quantity calculation result in the present embodiment.
The air conditioning trial calculation processing unit 142 in the present embodiment displays the calculation result of ΔT · heat quantity as shown on the display screen G6 of FIG. That is, the air conditioning trial calculation processing unit 142 calculates the temperature change amount ΔT, the cal converted value of the temperature change amount ΔT, and the J converted value, and generates and generates presentation information (display information) including the calorific value calculation results. The presentation information (display information) is output to the terminal device 10. Thereby, the terminal device 10 displays a display screen G6 as shown in FIG. 27 which is presentation information (display information).

  The derived model of heat quantity is expressed by the following equation (14). The variable V indicates a volume.

  The air conditioning trial calculation processing unit 142 calculates the heat amount as shown in the following equation (15) based on the equation (14).

  Here, the variable cp indicates the constant pressure specific heat of air (specific heat when the pressure of gas is constant). Furthermore, the air conditioning trial calculation processing unit 142 calculates a J converted value by the following equation (16).

Here, cal _th indicates a thermodynamic calorie (4.184: Japanese weighing method).
Note that when calculating the temperature change amount ΔT, the air conditioning trial calculation processing unit 142 acquires temperature information at any time by the temperature sensor 70 described above, and the temperature change in the area is stable based on the temperature setting of the air conditioner 50 and the like. The temperature change amount ΔT of the temperature change before and after at the point of time is calculated.
Further, the volume V is calculated by the following equation (17).

Next, a process of controlling the air conditioner 50 in the present embodiment via the AGCU unit 51 will be described.
FIG. 28 is a flow chart showing an example of control processing of air conditioning in the present embodiment.
In this figure, the air conditioning trial calculation processing unit 142 executes an air conditioning setting change designation process (step S601). Here, the air conditioning trial calculation processing unit 142 executes two processes of temperature and humidity setting (step S601A) and mode selection (step S601B).
Next, the air conditioning trial calculation processing unit 142 outputs a control command to the AGCU unit 51 of the air conditioner 50 (step S602).

  Specifically, in the temperature / humidity setting of step S601A, after the user designates the temperature and the humidity on the display screen G5 shown in FIG. 26, the air conditioning trial calculation processing unit is clicked by clicking the “control” button BT9. 142 obtains the specified temperature and humidity. Then, in step S602, the air conditioning trial calculation processing unit 142 outputs the temperature and the humidity designated by the AGCU unit 51 of the air conditioner 50, and executes control to change the setting information.

  In the mode selection in step S601B, the user selects one of the "high temperature mode" button BT5, the "low temperature mode" button BT6, and the "balance mode" button BT7 on the display screen G5 shown in FIG. By selecting the “control” button BT8 after selecting, the air conditioning trial calculation processing unit 142 acquires the selected setting mode. Then, in step S602, the air conditioning trial calculation processing unit 142 outputs the selected setting mode to the AGCU unit 51 of the air conditioner 50, and executes control to change the setting information.

As described above, in the power saving support system 1a according to the present embodiment, the power saving support apparatus 100a includes the learning information adding unit 114 that adds an explanatory variable different from power time-series data as learning information. Then, when learning information is added, the prediction processing unit 113 predicts future demand power and evaluates whether the added learning information is statistically valid.
As a result, the power saving support system 1a according to the present embodiment can add an explanatory variable and can quantitatively evaluate whether the explanatory variable is effective for the prediction of demand power using a statistical method. . Thereby, the prediction accuracy of the important power can be improved, and the application of unnecessary explanatory variables can be suppressed.

Further, in the present embodiment, the data collection device 200a collects measurement information on an area including at least power consumption in an area of a building. The power saving support device 100 a includes a trial calculation processing unit 140. The trial calculation processing unit 140 calculates the power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person based on the measurement information, and estimates the information on power saving based on the calculated power consumption of the device Make an estimate. Then, the trial calculation processing unit 140 outputs trial calculation presentation information including the trial calculation information to the terminal device 10 connected to the power saving support device 100 a via the network.
Thus, the power saving support system 1a according to the present embodiment can perform a trial calculation regarding power saving in consideration of the load felt by a person. Therefore, the power saving support system 1a according to the present embodiment can appropriately plan a power saving target or a power saving measure in consideration of the load felt by a person based on the demand power.

Further, in the present embodiment, the trial calculation processing unit 140 estimates the contribution ratio of the power consumption of the device to the power target value as trial calculation information based on the predetermined power target value and the power consumption of the device. The estimate presentation information including the contribution rate is output to the terminal device 10.
As a result, the power saving support system 1a according to the present embodiment can appropriately plan the power saving target or the power saving measure based on the contribution rate of the power consumption.

Further, in the present embodiment, the index indicating the load felt by a person includes the average illuminance, and the trial calculation processing unit 140 includes the lighting trial calculation processing unit 141. The lighting estimation processing unit 141 calculates the power consumption of the lighting device 60 of the building according to the average illuminance, and estimates lighting information based on the calculated power consumption of the lighting device 60. Then, the lighting trial calculation processing unit 141 outputs, to the terminal device 10, trial presentation information regarding lighting including the trial calculation information.
Thus, the power saving support system 1a according to the present embodiment can perform a trial calculation regarding power saving in consideration of work efficiency and comfort based on the average illuminance. Therefore, the power saving support system 1a in the present embodiment can appropriately plan the power saving target or the power saving measure in consideration of the work efficiency and the comfort according to the average illuminance.

Further, in the present embodiment, the lighting trial calculation processing unit 141 sets the first trial calculation information, which is trial calculation information regarding lighting when the average illuminance is the first reference value, and the case where the average illuminance is the second reference value. The second trial calculation information which is trial calculation information on the illumination is estimated, and the first estimation information calculated on the trial and the estimation presentation information on the illumination including the second estimation information are outputted to the terminal device 10.
Thereby, the power saving support system 1a in the present embodiment can compare the measures based on the two criteria of the average illuminance.

Further, in the present embodiment, one measurement device 40 is provided by the power sensor S1 (first sensor) that measures the used power of one of the plurality of lighting devices 60 provided in the area of the building. The total power consumption of the lighting device 60 is measured by the power sensor S2 (second sensor) that measures the total power consumption of the lighting device 60 provided in the area while measuring the power consumption of the lighting device 60. The data acquisition device 200a collects, for each area, the power consumption of one unit and the total power consumption of the lighting device 60 as measurement information. The lighting estimation processing unit 141 determines the required number of lighting devices 60 according to the average illuminance based on the space conditions of the area having the lighting devices 60, the power consumption for one unit, and the total power consumption of the lighting devices 60. It is calculated as trial calculation information, and trial calculation presentation information including at least the required number of lighting devices 60 is output to the terminal device 10.
Thus, the power saving support system 1a according to the present embodiment can estimate the required number of lighting devices 60 in consideration of work efficiency and comfort based on the average illuminance. Therefore, the power saving support system 1a in the present embodiment can efficiently plan an appropriate power saving target or a power saving measure.

Further, in the present embodiment, the index indicating the load felt by the person includes the discomfort index indicating the index felt by the person, which is calculated based on the temperature and the humidity, and the trial calculation processing unit 140 It has 142. Then, the air conditioning trial calculation unit 142 calculates the power consumption of the air conditioner 50 of the building according to the discomfort index, estimates the air conditioning information based on the calculated power consumption of the air conditioner 50, and estimates The estimate presentation information regarding the air conditioning including information is output to the terminal device 10.
Thus, the power saving support system 1a according to the present embodiment can perform a trial calculation regarding power saving in consideration of work efficiency and comfort based on the discomfort index. Therefore, the power saving support system 1a according to the present embodiment can appropriately plan the power saving target or the power saving measure in consideration of the work efficiency and the comfort according to the discomfort index.

Further, in the present embodiment, the measuring device 40 measures the power used by the air conditioner 50 and measures the temperature in the area by the temperature sensor that measures the temperature in the area of the building, thereby measuring the humidity in the area. The humidity in the area is measured by the humidity sensor. The data collection device 200a collects, for each area, the power used by the air conditioner 50, the temperature in the area, and the humidity in the area as measurement information. The air conditioning trial calculation processing unit 142 calculates the discomfort index based on the temperature in the area and the humidity in the area, and the discomfort index calculated based on the power consumption of the air conditioner 50 in the past collected by the data collection device 200. The power consumption of the air conditioner 50 according to the above is calculated.
As a result, the power saving support system 1a according to the present embodiment can estimate the power consumption of the air conditioner 50 in consideration of work efficiency and comfort based on the discomfort index. Therefore, the power saving support system 1a in the present embodiment can efficiently plan an appropriate power saving target or a power saving measure.

Further, the power saving support system 1a according to the present embodiment is based on the data collection device 200a for collecting measurement information on the area including at least the power used in the area of the building, and the building based on the measurement information collected by the data collection device 200a. And a power saving support device 100a that performs a trial calculation on power saving in the above. The power saving support device 100 a includes the trial calculation processing unit 140. The trial calculation processing unit 140 calculates the power consumption of the device related to the load felt by the person according to the index indicating the load felt by the person based on the measurement information, and estimates the information on power saving based on the calculated power consumption of the device Make an estimate.
Thus, the power saving support system 1a according to the present embodiment can perform a trial calculation regarding power saving in consideration of the load felt by a person. Therefore, the power saving support system 1a according to the present embodiment can appropriately plan a power saving target or a power saving measure in consideration of the load felt by a person based on the demand power. In addition, the power saving support system 1a in the present embodiment can appropriately plan a target or a countermeasure even in energy saving and CO 2 saving.

Third Embodiment
Next, a power saving support system according to a third embodiment will be described with reference to the drawings.
In the present embodiment, an evaluation process regarding power saving is further added to the second embodiment.

FIG. 29 is a block diagram showing an example of the power saving support system 1b according to the third embodiment.
In this figure, the power saving support system 1b includes a power saving support device 100b, a data collection device 200a, a plurality of measuring devices 40 (40-1, 40-2,...), And a plurality of air conditioners 50 (50-1, 50-2, ...). Further, the power saving support system 1 b includes a plurality of terminal devices 10 (10-1, 10-2,...) Connected to the power saving support device 100 b via the network N 1 and a FW (Fire Wall) device 20. The external cooperation server 300 connected with the power saving support device 100b, and the portable terminal devices 30 (30-1, 30-2,...) Are provided.
In addition, in this figure, the same code | symbol is attached | subjected about the structure same as the structure shown in FIG.1 and FIG.13, and the description is abbreviate | omitted. Hereinafter, configurations different from those in FIGS. 1 and 13 will be described.

The power saving support device 100 b includes a processing unit 110 b, a storage unit 120 a, and a measurement data acquisition unit 130.
The processing unit 110 b includes a learning data acquisition unit 111, an optimization processing unit 112, a prediction processing unit 113, a learning information adding unit 114, a trial calculation processing unit 140, and an evaluation processing unit 150. In addition, the evaluation processing unit 150 includes a power saving effort verification unit 151 and a power saving order evaluation unit 152.

The evaluation processing unit 150 calculates an evaluation index value relating to the power saving of the building before and after the power saving request based on the power consumption of the building measured by the measuring device 40 before and after the power saving request for requesting the cooperation of the power saving. The evaluation processing unit 150 outputs evaluation presentation information (for example, evaluation display information) including the evaluation result of power saving based on the calculated evaluation index value to the terminal device 10 connected with the power saving support device 100 b via the network.
Here, the evaluation index value is, for example, a t value in a t-test or a Spearman's rank correlation coefficient.

  Based on the power consumption of a plurality of buildings measured by the measuring device 40 before and after the power saving request, the power saving effort verification unit 151 significantly changes the change in the power consumption of the buildings before and after the power saving request among the plurality of buildings. Test information (for example, t value) for testing whether or not there is calculated as an evaluation index value. The power saving effort test unit 151 outputs the evaluation presentation information including the evaluation result based on the calculated test information to the terminal device 10.

For example, the power saving effort verification unit 151 analyzes whether there is a difference in power saving efforts in a plurality of buildings at different building sizes configured by a plurality of areas in a plurality of buildings.
Specifically, consider a regression analysis model such as the following equation (18).

Here, x _i (i is 1,... N) are existing explanatory variables, and α and β are unknown coefficients. Then, ε _i is a random error having an expected value “0” and an unknown variance σ 2 independently according to the same normal distribution. _{Further, Y} i (i is 1, · · · n) is the observed value. In this case, we want to test whether β is equal to a certain value β ₀ . In many cases, β ₀ is “0”. This is because if β is “0”, there is no correlation between x and y, and if β is a value other than “0”, then x and y are correlated. .
Here, if it is set as a following formula (19), _tscore will become a following formula (20).

Here, if the null hypothesis is correct, this numerical value follows the (n-2) degree of freedom of the t value. Furthermore, if it is set as a following formula (21), _tscore will become a following formula (22).

Here, if it is assumed that the null hypothesis is correct, a statistical test that uses the fact that the statistic follows the t distribution, that is, a t-test (test of difference between two groups) can be used.
In the present embodiment, using the verification model, the power saving effort verification unit 151 determines whether there is a difference in the power saving effort of the selected building unit by the user arbitrarily selecting the number of buildings n. Perform the related two-group test based on the data before and after the power saving request.

  The power saving rank evaluation unit 152 ranks the power consumption for each area based on the power consumption for each area in the building measured by the measuring device 40 before and after the power saving request, and ranks each before and after the power saving request The correlation coefficient of the used power for each area is calculated as an evaluation index value based on The power saving rank evaluation unit 152 outputs, to the terminal device 10, evaluation presentation information including the evaluation result based on the calculated correlation coefficient.

For example, the power saving rank evaluation unit 152 analyzes the correlation in the power saving rank in area units of a building. That is, the power saving order evaluation unit 152 obtains the relationship using the correlation index obtained from the order data in statistics. Specifically, the power saving rank evaluation unit 152 uses Spearman's rank correlation coefficient.
That is, in the present embodiment, a model such as the following equation (23) is considered.

  However, here, the variable D is the difference in the order of the values of the corresponding X and Y, and the variable N indicates the number of pairs of values (the number of pairs). This is a nonparametric index. That is, this model evaluates how closely the relationship between variables can be represented by any monotonous function without assuming anything about the distribution of two variables, and if the order is clear Good. Further, when the above equation (23) is modified, the following equation (24) is obtained.

The power saving rank evaluation unit 152 uses this test model to evaluate the correlation in the area-based power saving behavior of the building.
The power saving rank evaluation unit 152 evaluates whether or not the power saving rank of all the areas linked to the selected building has a correlation by the user selecting the relevant building. Based on the data before and after the power saving request, the power saving order evaluation unit 152 performs, as pre-processing, the power consumption order before the power saving request of each area (the area with the least consumption is regarded as the top and lower in order). Add a reduction rate order after the request for saving power in each area (the area with the highest reduction rate is taken as the top, in order from the lower). The power saving order evaluation unit 152 analyzes the correlation based on the order generated by this pre-processing. In addition, the reduction rate mentioned above is represented by following formula (25).

Next, the operation of the power saving support system 1b in the present embodiment will be described with reference to the drawings.
First, the analysis process of the power saving effort by the power saving effort verification unit 151 will be described.
<Analytical processing of power saving efforts>
FIG. 30 is a flowchart showing an example of analysis processing of power saving effort in the present embodiment. FIG. 31 is a view showing an example of a display screen of an analysis result in the analysis process of the power saving effort in the present embodiment.

  In FIG. 30, first, the power saving effort verification unit 151 selects a building by the user (step S701). That is, the power saving effort test unit 151 causes the terminal device 10 to display the display screen G7 shown in FIG. 31, and the user selects a building on the display screen G7. The power saving effort verification unit 151 acquires information on the selected building via the terminal device 10.

  Next, the power saving effort verification unit 151 starts analysis processing in response to the power saving request (step S702). That is, the power saving effort verifying unit 151 starts the above-described analysis processing based on the t-test, using the timing when the user clicks the “power saving request” button BT10 as a key.

  Next, the power saving effort verification unit 151 acquires measurement data before and after the power saving request (step S703). That is, the power saving effort test unit 151 acquires measurement data at the time of clicking the “power saving request” button BT10 described above and one hour after that.

  Next, the power saving effort test unit 151 executes the above-described analysis process by the t-test based on the acquired measurement data before and after the power saving request (step S704). Based on the data before and after the power saving request, the power saving effort verifying unit 151 determines whether there is a difference in the power saving effort of the selected building unit using the verification model shown in the above-mentioned equation (22). Perform two groups of tests.

  Next, the power saving effort test unit 151 displays the test result (step S705). That is, the power saving effort test unit 151 generates evaluation presentation information including the test result by the t-test, and outputs the evaluation presentation information to the terminal device 10. As a result, the display screen G7 shown in FIG. 31 is displayed on the terminal device 10, and the test result is presented to the user.

Next, analysis processing of the power saving order correlation coefficient by the power saving order evaluation unit 152 will be described.
<Analysis processing of power saving order correlation coefficient>
FIG. 32 is a flowchart showing an example of analysis processing of the power saving order correlation coefficient in the present embodiment. FIG. 33 is a view showing an example of a display screen of an analysis result in the analysis processing of the power saving order correlation coefficient in the present embodiment.

  In FIG. 32, first, the power saving order evaluation unit 152 selects a building by the user (step S801). That is, the power saving effort verifying unit 151 causes the terminal device 10 to display the display screen G8 shown in FIG. 33, and the user selects a building on the display screen G8, whereby the area is selected. The power saving order evaluation unit 152 acquires information on the selected building (all area information) via the terminal device 10.

  Next, the power saving rank evaluation unit 152 starts an analysis process in response to the power saving request (step S802). That is, the power saving rank evaluation unit 152 starts the analysis processing based on the Spearman rank correlation coefficient described above, using the timing when the user clicks the “power saving request” button BT11.

  Next, the power saving rank evaluation unit 152 acquires measurement data before and after the power saving request (step S803). That is, the power saving order evaluation unit 152 acquires measurement data when the above-mentioned "power saving request" button BT11 is clicked and one hour after that.

  Next, based on the measurement data before and after the acquired power saving request, the power saving rank evaluation unit 152 performs the above-described pre-processing (ranking processing) (step S804), and then performs correlation analysis using the Spearman rank correlation coefficient described above. A process is performed (step S805). The power saving effort verification unit 151 analyzes whether the power saving behavior of the entire area in the selected building is correlated or not by using the correlation analysis based on the order shown in the above-mentioned equation (24).

  Next, the power saving order evaluation unit 152 displays the analysis result (step S806). That is, the power saving order evaluation unit 152 generates evaluation presentation information including an analysis result based on Spearman's rank correlation coefficient, and outputs the evaluation presentation information to the terminal device 10. As a result, the display screen G8 shown in FIG. 33 is displayed on the terminal device 10, and the analysis result is presented to the user.

As described above, in the power saving support system 1b according to the present embodiment, the power saving support apparatus 100b includes the evaluation processing unit 150. The evaluation processing unit 150 calculates an evaluation index value regarding the power saving of the building before and after the power saving request based on the power consumption of the building measured by the measuring device 40 before and after the power saving request. The evaluation processing unit 150 outputs, to the terminal device 10, evaluation presentation information including the evaluation result of the power saving based on the calculated evaluation index value.
Thereby, the power saving support system 1b according to the present embodiment can quantitatively evaluate the evaluation of the power saving behavior. Since the power saving behavior can be appropriately evaluated, the power saving support system 1b according to the present embodiment can appropriately plan the power saving target or the power saving measure based on the evaluation result. In addition, the power saving support system 1b according to the present embodiment can appropriately draft a target or a countermeasure even in energy saving and CO 2 saving.

In addition, the power saving support system 1b in the present embodiment includes a plurality of data collection devices 200a, and each of the plurality of data collection devices 200a collects power consumption of each of a plurality of buildings. The evaluation processing unit 150 is provided with the power saving effort verification unit 151. Based on the power consumption of a plurality of buildings measured by the measuring device 40 before and after the power saving request, the power saving effort verification unit 151 significantly changes the change in the power consumption of the buildings before and after the power saving request among the plurality of buildings. Test information for testing whether or not there is calculated as an evaluation index value. The power saving effort test unit 151 outputs the evaluation presentation information including the evaluation result based on the calculated test information to the terminal device 10.
Thus, the power saving support system 1b according to the present embodiment can quantitatively evaluate whether there is a difference in power saving efforts between buildings.

Further, in the power saving support system 1b according to the present embodiment, the building has a plurality of areas, and the data collection device 200a collects time series data of power for each area. Then, the evaluation processing unit 150 includes the power saving order evaluation unit 152. The power saving rank evaluation unit 152 ranks the power consumption for each area based on the power consumption for each area in the building measured by the measuring device 40 before and after the power saving request, and ranks each before and after the power saving request The correlation coefficient of the used power for each area is calculated as an evaluation index value based on The power saving rank evaluation unit 152 outputs, to the terminal device 10, evaluation presentation information including the evaluation result based on the calculated correlation coefficient.
Thereby, the power saving support system 1b in the present embodiment can quantitatively evaluate whether or not there is a correlation with the power saving behavior in the entire area in the building.

Further, the power saving support system 1b in the present embodiment is a building based on the data collection device 200a for collecting the power used in the building by at least the measuring device 40 for measuring the power, and the power used for the building collected by the data collection device 200a. And a power saving support apparatus 100b that performs evaluation on power saving in The power saving support apparatus 100 b includes an evaluation processing unit 150. The evaluation processing unit 150 calculates an evaluation index value regarding the power saving of the building before and after the power saving request based on the power consumption of the building measured by the measuring device 40 before and after the power saving request. The evaluation processing unit 150 outputs the evaluation presentation information including the evaluation result of the power saving based on the calculated evaluation index value.
Thereby, the power saving support system 1b according to the present embodiment can quantitatively evaluate the evaluation of the power saving behavior. Since the power saving behavior can be appropriately evaluated, the power saving support system 1b according to the present embodiment can appropriately plan the power saving target or the power saving measure based on the evaluation result.

In addition, this invention is not limited to said each embodiment, It can change in the range which does not deviate from the meaning of this invention.
For example, although an example using a linear multiple regression model was described as a statistical model of prediction processing in each above-mentioned embodiment, it is not limited to this. For example, as a statistical model of prediction processing, a discriminant model, a classification tree (decision tree), an autoregressive model, a moving average autoregressive model, a neural network model, a support vector regression model, or the like may be applied.
Further, the predetermined time interval, the predetermined learning range, the first period period, and the second period period are not limited to the above embodiments, and can be changed without departing from the scope of the present invention. It is.

In each of the above-described embodiments, the power saving support device 100 (100a, 100b) includes the storage unit 120. However, all or part of the storage unit 120 in the power saving support device 100 (100a, 100b) You may prepare it outside. For example, the storage unit 120 (120a) may be a file server on a network.
In each of the above embodiments, the data collection device 200 (200a) includes the collected data storage unit 220. However, all or a part of the collected data storage unit 220 may be used as the data collection device 200 (200a). You may prepare it outside.

  In the above embodiment, the case of converting temperature change amount ΔT into cal conversion and J conversion has been described, but it may be converted to CO2 (carbon dioxide) emissions, or crude oil conversion or W (watts) conversion May be

In each of the above embodiments, when there is a loss (lost) in the time-series data corresponding to the explanatory variable, the optimization processing unit 112 interpolates the moving average of two data before and after the time of the loss. Although an example has been described, the present invention is not limited to this. For example, the optimization processing unit 112 may use a moving average of a total of four data, two each before and after. Further, the optimization processing unit 112 may interpolate missing data using an approximation function.
In each of the above-described embodiments, although the case where each process is started by the user's operation has been described, the process may be automatically started using a power saving request as a trigger.
In each of the above embodiments, the prediction processing, the estimation processing, and the evaluation processing are performed in units of buildings or in units of areas, but each processing may be performed in units of rooms, towns, cities, etc., for example. It may apply.

A program for realizing the function of each component included in the power saving support system 1 (1a, 1b) according to the present invention is recorded in a computer readable recording medium, and the program recorded in the recording medium is recorded in a computer system. The processing in each configuration included in the above-described power saving support system 1 (1a, 1b) may be performed by reading and executing. Here, "to read and execute the program recorded on the recording medium into the computer system" includes installing the program on the computer system. The “computer system” mentioned here includes an OS and hardware such as peripheral devices.
Also, the “computer system” may include a plurality of computer devices connected via a network including communication lines such as the Internet, WAN, LAN, and dedicated lines. The term "computer-readable recording medium" refers to a storage medium such as a flexible disk, a magneto-optical disk, a ROM, a portable medium such as a ROM or a CD-ROM, or a hard disk built in a computer system. As described above, the recording medium storing the program may be a non-transitory recording medium such as a CD-ROM.

  The recording medium also includes a recording medium provided internally or externally accessible from the distribution server for distributing the program. The program is divided into a plurality of parts, downloaded at different timings, and then the configurations combined in each configuration of the power saving support system 1 (1a, 1b) and the distribution server for distributing each of the divided programs are different. May be Furthermore, "computer-readable recording medium" holds a program for a certain period of time, such as a volatile memory (RAM) in a computer system serving as a server or a client when the program is transmitted via a network. We shall include things. Further, the program may be for realizing a part of the functions described above. Furthermore, it may be a so-called difference file (difference program) that can realize the above-described functions in combination with a program already recorded in the computer system.

  In addition, some or all of the functions described above may be realized as an integrated circuit such as LSI (Large Scale Integration). Each function mentioned above may be processor-ized separately, and part or all may be integrated and processor-ized. Further, the method of circuit integration is not limited to LSI's, and implementation using dedicated circuitry or general purpose processors is also possible. In the case where an integrated circuit technology comes out to replace LSI's as a result of the advancement of semiconductor technology, integrated circuits based on such technology may also be used.

1, 1a, 1b Power saving support system 10, 10-1, 10-2 Terminal device 20 FW device 30, 30-1, 30-2 Portable terminal device 40, 40-1, 40-2 Measuring device 41 Sensor GW unit 42 Distribution board 50, 50-1, 50-2 Air conditioner 51 AGCU section 60, 60-1, 60-2, 60-3 Lighting equipment 70, 70-1, 70-2, 70-3, 70-4, 70-5, 70-6 Temperature sensor 80, 80-1, 80-2 Humidity sensor 100, 100a, 100b Power saving support device 110, 110a, 110b Processing unit 111 Learning data acquisition unit 112 Optimization processing unit 113 Prediction processing unit 120 , 120a storage unit 121 power information storage unit 122 calendar information storage unit 123 setting information storage unit 124 prediction result storage unit 125 air conditioning information storage unit 130 measurement data acquisition Part 140 Trial calculation processing part 141 Lighting calculation processing part 142 Air conditioning calculation processing part 150 Evaluation processing part 151 Power saving effort test part 152 Power saving order evaluation part 200, 200a Data collection device 210, 210a Collection processing part 211, 211a Power collection part 212 Air conditioning information Collection unit 213 Air conditioning control unit 220 Collection data storage unit A1 Area B1 Building N1, N2 Network S1, S2 Power sensor

Claims (3)

A data collection device for collecting power consumption of a building by at least measuring equipment for measuring power;
And a power saving support apparatus that performs evaluation on power saving in the building based on the power consumption of the building collected by the data collection apparatus.
The power saving support device is
Based on the power consumption of the building measured by the measuring device before and after the power saving request requesting cooperation for power saving, the evaluation index value regarding the power saving of the building before and after the power saving request is calculated and calculated An evaluation processing unit that outputs evaluation presentation information including an evaluation result of power saving based on a value ;
The building has a plurality of areas,
The data collection device collects time series data of the power for each area;
The evaluation processing unit
Based on the power consumption for each area in the building measured by the measuring device before and after the power saving request, the power consumption for each area is ranked according to the power consumption before the power saving request for each area, and The rank of reduction rate of power consumption after power saving request in each area is ranked, and the ranking of power consumption before the power saving request and the power usage after the power saving request are ranked before and after the power saving request respectively Based on the reduction rate rank, a rank correlation coefficient is calculated as the evaluation index value, and the evaluation presentation information including the evaluation result based on the calculated rank correlation coefficient is transmitted via the power saving support device and the network. Power saving order evaluation unit for outputting to the connected terminal device
Power-saving support system comprising a call. Comprising a plurality of said data acquisition devices;
Each of the plurality of data collection devices collects power consumption of each of the plurality of buildings,
The evaluation processing unit
Based on the power consumption of the plurality of buildings measured by the measuring device before and after the power saving request, changes in the power usage of the buildings before and after the power saving request are significantly different among the plurality of buildings A power saving effort test unit that calculates test information for testing whether or not the evaluation indicator value, and outputs the evaluation presentation information including the evaluation result based on the calculated test information to the terminal device. The power saving support system according to claim 1. At least power based power usage of a building having a plurality of areas by the measuring instrument for measuring the power used for the building of the data collection device collects to collect each of the areas a, power saving support device that performs an evaluation of the power saving in the building And
Based on the power consumption of the building measured by the measuring device before and after the power saving request requesting cooperation for power saving, the evaluation index value regarding the power saving of the building before and after the power saving request is calculated and calculated An evaluation processing unit that outputs evaluation presentation information including an evaluation result of power saving based on a value ;
The evaluation processing unit
Based on the power consumption for each area in the building measured by the measuring device before and after the power saving request, the power consumption for each area is ranked according to the power consumption before the power saving request for each area, and The rank of reduction rate of power consumption after power saving request in each area is ranked, and the ranking of power consumption before the power saving request and the power usage after the power saving request are ranked before and after the power saving request respectively Based on the reduction rate rank, a rank correlation coefficient is calculated as the evaluation index value, and the evaluation presentation information including the evaluation result based on the calculated rank correlation coefficient is transmitted via the power saving support device and the network. Output to the connected terminal device
Power-saving support device comprising a call.

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