Energy saving effect estimation system and control method thereof

Abstract

To provide a technique capable of accurately estimating a reduction effect of power consumption of a building when an air conditioning facility is controlled in an energy saving manner without adding a dedicated facility.SOLUTION: CPU101 generates normalized information (normalized received energy 311 to 321 and normalized air conditioning energy 312 to 322) obtained by normalizing the power consumption and the air-conditioning power energy in the plurality of buildings. The CPU101 extracts similar buildings having a tendency of power consumption similar to that of the target building from the plurality of buildings. CPU101 generates a model 70 of the amount of received power 71 and the amount of air-conditioning power 72 of the target building. CPU101 estimates the reduction effect (estimation result 80) by inputting the reduction ratio (calculation condition 60) of the air-conditioning power amount by the energy saving control to the model 70.SELECTED DRAWING: Figure 4

Description

[Technical Field]

[0001] The present disclosure relates to an energy saving effect estimation system that estimates the effect of reducing the amount of power consumption of a target building, and a control method thereof. [Background technology]

[0002] There is a need to estimate the energy-saving effect of operating the air conditioning equipment of a building using energy-saving controls, such as how much the power consumption of the entire building will be reduced.When estimating this energy-saving effect, it is necessary to use the actual past power consumption values ​​of the air conditioning equipment measured in the building. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Laid-Open No. 2011-34484 Summary of the Invention [Problem to be solved by the invention]

[0004] However, in order to measure the actual power consumption of air conditioning equipment, it is necessary to install dedicated equipment such as building management devices, which poses the problem of enormous costs being incurred in estimating the energy-saving effects.

[0005] The present disclosure has been made to solve such problems, and the purpose of the present disclosure is to provide a technology that can accurately estimate the reduction in power consumption of a building when air conditioning equipment is controlled in an energy-saving manner, without the need to add dedicated equipment. [Means for solving the problem]

[0006] The energy-saving effect estimation system disclosed herein is a system that estimates the reduction effect of power consumption of a target building. The energy-saving effect estimation system includes a storage device and a processing device. The storage device stores feature data, which is data related to the power consumption of the target building, and building data for each of a plurality of buildings other than the target building. The processing device estimates the reduction effect of power consumption of the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls energy conservation. The building data for each of the plurality of buildings includes the building's power consumption and the amount of air conditioning power consumed by the air conditioning equipment within the building's power consumption. The processing device generates normalized data that normalizes the power consumption and air conditioning power of the plurality of buildings. The processing device uses the feature data to extract, from the plurality of buildings, similar buildings whose power consumption trends are similar to that of the target building. The processing device uses the normalized data and feature data of the similar buildings to generate a model of the power consumption and air conditioning power of the target building. The processing device estimates the reduction effect by inputting the reduction rate of air conditioning power due to energy-saving control into the model.

[0007] The present disclosure also provides a control method for an energy-saving effect estimation system that estimates the reduction effect of power consumption of a target building. The energy-saving effect estimation system includes a storage device that stores feature data related to the power consumption of the target building and building data for each of a plurality of buildings other than the target building. The control method includes a step of estimating the reduction effect of power consumption of the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls energy conservation. The building data for each of the plurality of buildings includes the building's power consumption and the amount of air conditioning power consumed by the air conditioning equipment within the building's power consumption. The estimating step includes a step of generating normalized data by normalizing the power consumption and air conditioning power of the plurality of buildings; a step of using the feature data to extract similar buildings from the plurality of buildings that have a power consumption trend similar to that of the target building; a step of generating a model of the power consumption and air conditioning power of the target building using the normalized data and feature data of the similar buildings; and a step of estimating the reduction effect by inputting the reduction rate of air conditioning power due to energy-saving control into the model. [Effects of the Invention]

[0008] According to the present disclosure, it is possible to accurately estimate the reduction effect of the amount of power consumption of a building when air conditioning equipment is controlled in an energy-saving manner, without adding dedicated equipment. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram for explaining a conventional method for estimating energy saving effects. [Figure 2] 10 is a diagram for explaining the relationship between energy saving control to be executed and the reduction rate of the amount of power consumption. FIG. [Figure 3] 10 is a graph showing daily trends in the amount of received power and the amount of air conditioning power. [Figure 4] FIG. 2 is a diagram for explaining the flow of data processing in the energy saving effect estimation system according to the first embodiment. [Figure 5] FIG. 2 is a diagram illustrating a hardware configuration of the energy-saving effect estimation system. [Figure 6] 10 is a graph showing a daily trend in normalized air conditioning power consumption. [Figure 7] 10 is a graph showing a daily trend of normalized received power amount. [Figure 8] 10 is a flowchart of a process executed by the energy saving effect estimation system. [Figure 9] FIG. 10 is a diagram for explaining the flow of data processing in the energy saving effect estimation system according to the second embodiment. [Figure 10] 10 is a flowchart of a process executed by the energy saving effect estimation system. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, embodiments of the present disclosure will be described in detail with reference to the drawings. While several embodiments will be described below, it was originally intended that the configurations described in each embodiment be combined as appropriate. Note that identical or corresponding parts in the drawings will be designated by the same reference numerals, and their description will not be repeated.

[0011] [First embodiment] First, a conventional method for estimating the energy-saving effect (how much the power consumption of the entire building is reduced) when the air conditioning equipment of a target building (hereinafter also referred to as "target building") is operated under energy-saving control will be described. In the following, in this embodiment, a "building" is used as an example of a building, and the "target building" will also be referred to as a "target building."

[0012] Fig. 1 is a diagram for explaining a conventional method for estimating energy-saving effects. In Fig. 1, an estimated result 80 of the energy-saving effects of a target building is obtained based on target building data 10 and calculation conditions 60 for the energy-saving effects.

[0013] When calculating the energy saving effect, it is usually necessary to accumulate the actual value of the amount of electricity consumed by the target building for which the energy saving effect is to be calculated as the target building data 10. The target building data 10 records the contracted power 11, the amount of received electricity 12, and the amount of air conditioning electricity 13 as data.

[0014] The contracted power 11 is the contracted power determined between the electric power company and the target building. The contracted power 11 is set to the maximum value of the power consumption (measured every 30 minutes) for the previous year. The received power amount 12 indicates the power consumption of the target building (the total power consumption of the target building = the amount of power received by the target building).

[0015] Air conditioning power consumption 13 indicates the amount of power consumed by the air conditioning equipment installed in the target building out of the power consumption of the target building (received power consumption 12). Both contracted power 11 and air conditioning power consumption 13 are time-series data that show the trend in power consumption per unit time (30 minutes), and data for at least the previous year is accumulated. In order to record air conditioning power consumption 13, it is necessary to install dedicated equipment such as a building management device.

[0016] The calculation conditions 60 include energy saving control settings 61 and reduction rates 62 for each energy saving control. The energy saving control settings 61 are settings for energy saving control of air conditioning equipment executed in the target building. The reduction rates 62 for each energy saving control are data indicating the reduction rates of air conditioning power consumption corresponding to each energy saving control. Details will be described later using FIG. 2.

[0017] The estimated result 80 includes a power reduction effect 81 and a peak power reduction effect 82. The power reduction effect 81 is data showing the power consumption reduction effect of the energy saving control set in the target building, such as the reduction rate of the annual power consumption (annual amount of received power) of the target building when the energy saving control set in the energy saving control setting 61 is implemented. The power consumption reduction effect 81 may include data such as the annual power consumption of the target building when energy saving control is implemented and the annual power consumption of the target building when energy saving control is not implemented.

[0018] The peak power reduction effect 82 is data showing the peak power reduction effect of the energy saving control set in the target building, such as the reduction rate of the peak power of the target building when the energy saving control set in the energy saving control setting 61 is implemented. The peak power reduction effect 82 may include data such as the peak power of the target building when energy saving control is implemented and the peak power of the target building when energy saving control is not implemented. Here, "peak power" refers to the maximum value (also referred to as "annual maximum value") of the amount of power consumed (amount of received power) of the target building in one year.

[0019] 2 is a diagram for explaining the relationship between the energy saving control to be executed and the reduction rate of the amount of power consumption. Reduction rate 62 for each energy saving control shows the relationship between various energy saving controls that can be executed and the reduction rate of the amount of power consumption for air conditioning when the energy saving control is executed.

[0020] Energy-saving control is control that operates the air conditioners in the target building with less energy than during normal operation. Energy-saving control includes indoor unit air flow control. Indoor unit air flow control includes indoor unit air flow control (weak), indoor unit air flow control (medium), and indoor unit air flow control (strong).

[0021] The reduction rate 62 for each energy-saving control indicates that when indoor unit airflow control (weak) is performed, the amount of air conditioning power is reduced by 5%. When indoor unit airflow control (medium) is performed, the amount of air conditioning power is reduced by 15%. When indoor unit airflow control (strong) is performed, the amount of air conditioning power is reduced by 30%. In addition to airflow control, energy-saving control also includes energy-saving control such as intermittently stopping the air conditioner.

[0022] Figure 3 is a graph showing the daily trends in the amount of received power and the amount of air conditioning power. The horizontal axis of graph 21 in Figure 3 shows the time of day from 0:00 to 24:00. The vertical axis of graph 21 shows the amount of received power (unit: kWh) and the amount of air conditioning power (unit: kWh).

[0023] Graph 21 shows an example plotted based on the received power amount 12 and air conditioning power amount 13 of the target building data 10. The target building data 10 records data in 30-minute increments for a period (the previous year), and graph 21 shows the trend in the average value of the power amount for each time period on a monthly basis (in this example, the trend in data for August).

[0024] Graph 21 shows that the average amount of received power from 0:00 to 0:30 on August 1st to 31st was 30 kWh, and the average amount of air conditioning power was 10 kWh. It also shows that the average amount of received power from 0:30 to 1:00 on August 1st to 31st was 30 kWh, and the average amount of air conditioning power was 12 kWh. It also shows that the average amount of received power from 23:30 to 24:00 on August 1st to 31st was 40 kWh, and the average amount of air conditioning power was 20 kWh.

[0025] In this way, the target building data 10 records daily trend data of received power amount 12 and daily trend data of air conditioning power amount 13 from January to December. In this example, the maximum value (annual maximum value) of received power amount of the target building data 10 for the previous year is "420 kWh", and "420 kWh" is recorded as the contracted power 11 of the target building data 10.

[0026] For example, assume that "indoor unit fan control (high)" is set as the energy saving control setting 61 in the calculation condition 60. In this case, based on the reduction rate 62 for each energy saving control, a reduction rate of 30% in the air conditioning power consumption is obtained.

[0027] In this case, for example, in the example of Figure 3, the air conditioning power amount from 0:00 to 0:30 is reduced by 30% (3kWh = 10 x 0.3) from 10kWh to 7kWh (= 10-3). Because the air conditioning power amount is reduced by 3kWh, the received power amount is also reduced by 3kWh, and the received power amount is 27kWh (= 30-3).

[0028] The air conditioning power consumption from 13:00 to 13:30 is reduced by 30% (75kWh = 250 x 0.3) from 250kWh to 175kWh (= 250 - 75). Because the air conditioning power consumption is reduced by 75kWh, the received power consumption is also reduced by 75kWh, resulting in a received power consumption of 345kWh (= 420 - 75). This received power consumption of 420kWh is the maximum value of the received power consumption.

[0029] In this way, the amount of received power in each time period after application of energy saving control is calculated based on the energy saving control set in energy saving control setting 61. Then, the amount of received power per day in August is calculated by accumulating the amount of received power in each time period, and the amount of received power for the entire month of August is calculated by multiplying this by the number of days (31 days). In this way, the amount of received power for each month is calculated and accumulated to calculate the amount of received power for one year.

[0030] The reduction rate of the target building's annual power consumption (annual amount of power received) = (1 - (amount of power received in one year when energy-saving control is applied) / (amount of power received in one year when energy-saving control is not applied)) x 100 [%]. Data such as this data that compares the amount of power received before and after the application of energy-saving control is recorded as the power reduction effect 81.

[0031] Meanwhile, the maximum amount of received power is 420 kWh. In the above calculation, the maximum amount of received power has been reduced from 420 kWh to 345 kWh. In this case, the reduction rate of peak power for the target building = (1 - 345 / 420) x 100 = 17.9%. Data such as this data, which compares the annual maximum amount of received power before and after the application of energy-saving controls, is recorded as the reduction effect of peak power (maximum amount of received power) 82. By reducing peak power, it is possible to lower the contracted power for the following year.

[0032] The following describes the processing executed by the energy saving effect estimation system 100 according to the first embodiment. Fig. 4 is a diagram for explaining the flow of data processing in the energy saving effect estimation system 100 according to the first embodiment. The energy saving effect estimation system 100 is a system that estimates the reduction effect (energy saving effect) of power consumption in a target building (target building) when the air conditioning equipment of the target building is operated under energy saving control that controls the air conditioning equipment in an energy-saving manner.

[0033] In the conventional method of estimating energy-saving effects explained in Fig. 1, actual data on the amount of power received 12 and the amount of power consumed by air conditioning 13 of the target building was required. On the other hand, in the energy-saving effect estimation system 100, these data are not required. Therefore, it is possible to estimate energy-saving effects without adding dedicated equipment that has high installation costs.

[0034] Instead, the energy saving effect estimation system 100 uses data (model calculation data 200) of multiple properties for which actual data has already been acquired to estimate a model 70 (received power amount 71 and air conditioning power amount 72) equivalent to the data of the target building's received power amount 12 and air conditioning power amount 13. Details will be explained below.

[0035] The energy saving effect estimation system 100 stores target building data 50, model calculation data 200, 300, similar property average 40, model 70, calculation conditions 60, and trial calculation results 80.

[0036] The energy saving effect estimation system 100 calculates an estimated calculation result 80 using target building data 50 and calculation conditions 60, as in the explanation of FIG. 1. However, unlike the target building data 10 (FIG. 1), the target building data 50 does not record actual data on the amount of received power and the amount of air conditioning power. The target building data 50 holds the contracted power 51 and building information 52 of the target building as feature data. The feature data is data related to the amount of power consumed by the target building.

[0037] The building information 52 is information that indicates the characteristics of the target building. The building information 52 includes the building use and total floor area. The building use indicates the use of the building, such as a commercial building, office building, hospital, school, etc. Buildings with the same building use and similar total floor areas tend to show similar consumption patterns for the amount of received power and the amount of air conditioning power.

[0038] The model calculation data 200 is data that records building data (building X1 data 210 to building XN data 220) for each of a plurality of buildings (building X1 to building XN) different from the target building. The plurality of buildings are N buildings: building X1, building X2, ... building XN. The model calculation data 200 includes building X1 data 210 for building X1 to building XN data 220 for building XN.

[0039] The building X1 data 210 includes a received power amount 211, an air conditioning power amount 212, and building information 213 of the building X1. The building XN data 220 includes a received power amount 221, an air conditioning power amount 222, and building information 223 of the building XN.

[0040] In this way, each building data (Building X1 data 210 to Building XN data 220) of multiple buildings (Building X1 to Building XN) different from the target building includes the building's received power amount (Received power amount 211 to 221), the air conditioning power amount consumed by the air conditioning equipment out of the building's received power amount (Air conditioning power amount 212 to 222), and the building's use and total floor area (Building information 313 to Building information 323).

[0041] 5 is a diagram showing the hardware configuration of the energy-saving effect estimation system 100. The energy-saving effect estimation system 100 includes a CPU (Central Processing Unit) 101 as a processing device, a RAM (Random Access Memory) 102, a ROM (Read Only Memory) 103, an I / F (Interface) device 104, and a storage device 105. The CPU 101, RAM 102, ROM 103, I / F device 104, and storage device 105 exchange various types of data via a communication bus 106.

[0042] The CPU 101 loads a program stored in the ROM 103 or the storage device 105 into the RAM 102 and executes it. The program stored in the ROM 103 or the storage device 105 describes processes to be executed by the energy-saving effect estimation system 100. The I / F device 104 is an input / output device for exchanging signals and data with each device. The storage device 105 is a storage for storing various types of information.

[0043] The storage device 105 stores target building data 50, calculation conditions 60, trial calculation results 80, similar property average 40, model 70, and model calculation data 200, 300. The energy saving effect estimation system 100 may be configured, for example, by one server device or by multiple server devices. Furthermore, the storage device 105 may be configured by multiple storage devices, and the CPU 101 may be configured by multiple CPUs.

[0044] Returning to FIG. 4, the received power amount 211, the air conditioning power amount 212, etc. are data in the same format as the received power amount 12 and the air conditioning power amount 13 explained in FIG. 1, and are data for the 12 months of the previous year as shown in the graph 21 in FIG. 3. The building information 213, etc., includes, for example, the building use "office building" and the total floor area "A1m 2 " Information such as this is recorded.

[0045] In this embodiment, the energy saving effect estimation system 100 generates normalized data (normalized received power amounts 311-321, normalized air conditioning amounts 312-322) by normalizing the received power amounts and air conditioning power amounts of a plurality of buildings different from the target building. The energy saving effect estimation system 100 uses feature data to extract, from the plurality of buildings, similar buildings whose received power amount trends are similar to that of the target building. The energy saving effect estimation system 100 generates a model 70 of received power amount 71 and air conditioning power amount 72 of the target building using the normalized data of the similar buildings and the feature data. The energy saving effect estimation system 100 estimates the reduction effect (estimated calculation result 80) by inputting the reduction rate of air conditioning power amount due to energy saving control (calculation condition 60) into the model 70. This will be explained in detail below.

[0046] First, the energy saving effect estimation system 100 normalizes each building data of the model calculation data 200 to calculate model calculation data 300 (normalized data). The normalized data includes normalized received power amounts (normalized received power amounts 311-321) obtained by normalizing the received power amounts (received power amounts 211-221) and normalized air conditioning amounts (normalized air conditioning amounts 312-322) obtained by normalizing the air conditioning power amounts (air conditioning power amounts 212-222).

[0047] The normalized received power amount is time series data obtained by dividing each value of the received power amount by the annual maximum value of the received power amount (normalized received power amount = received power amount / annual maximum value of received power amount). The normalized air conditioning amount is time series data obtained by dividing each value of the air conditioning power amount by the corresponding value of the received power amount (normalized air conditioning amount = air conditioning power amount / received power amount).

[0048] For example, the normalized received power amount 311 of the building X1 data 310 is a value (time series data) obtained by dividing each value of the received power amount 211 of the building X1 data 210 by the annual maximum value of the received power amount of building X1. The normalized air conditioning amount 312 of the building X1 data 310 is a value (time series data) obtained by dividing each value of the air conditioning power amount 212 of the building X1 data 210 by the corresponding value of the received power amount 211. The building information 313 is the same as the building information 213. The same applies to the building XN data 320, etc.

[0049] Fig. 6 is a graph showing the daily trend of normalized air conditioning power consumption. Graph 22 shown in Fig. 6 is a plot of normalized air conditioning power consumption obtained by normalizing the air conditioning power consumption of graph 21 shown in Fig. 3.

[0050] Here, graph 21 in Figure 3 is data plotted based on the air conditioning power amount 212 and received power amount 211 of building X1 data 210, and graphs 22 and 23 in Figures 6 and 7 (described later) are data plotted based on the normalized air conditioning amount 312 and normalized received power amount 311 of building X1 data 310.

[0051] For example, the air conditioning power consumption from 0:00 to 0:30 in graph 21 (FIG. 3) is 10 kWh. The corresponding normalized air conditioning capacity in graph 22 (FIG. 6) is 10 / 30 = 0.33. The air conditioning power consumption from 0:30 to 1:00 in graph 21 is 12 kWh. The corresponding normalized air conditioning capacity is 12 / 30 = 0.4.

[0052] Figure 7 is a graph showing the daily trend of normalized received power amount. Graph 23 shown in Figure 7 is a plot of normalized received power amount obtained by normalizing the received power amount of graph 21 shown in Figure 3. Here, it is assumed that the annual maximum value of received power amount is 420 kWh.

[0053] For example, the amount of received power from 0:00 to 0:30 in graph 21 (Fig. 3) is 30 kWh. The corresponding normalized amount of received power in graph 23 (Fig. 7) is 30 / 420 = 0.07. The corresponding normalized amount of received power from 13:00 to 13:30 in graph 21 is 420 kWh. The corresponding normalized amount of received power = 420 / 420 = 1.

[0054] Returning to FIG. 4, the energy saving effect estimation system 100 generates a similar property average 40 based on the model calculation data 300 and the building information 52 of the target building data 50.

[0055] First, the energy saving effect estimation system 100 extracts building data whose building information is similar to that of the target building data 50. Specifically, from the building information 313 of the building X1 data 310 to the building information 313 of the building XN data 320, the building information 313 similar to the building information 52 of the target building data 50 is extracted.

[0056] Specifically, the energy saving effect estimation system 100 extracts, from among the multiple buildings, buildings whose building use matches that of the target building and whose difference in total floor area from that of the target building is within a predetermined range as similar buildings. For example, if the building use of "office building" and the total floor area of ​​"A1 square meters" are recorded in the building information 52, building data whose building use matches (is "office building") and whose total floor area is close to "A1" (for example, a total floor area in the range of A1 x 90% to A1 x 110%) may be extracted as similar properties (also referred to as "similar buildings").

[0057] The total floor area may be classified into small, medium, and large floor areas according to its size, and buildings with the same building use and the same floor area classification may be extracted as similar properties. Furthermore, items other than building use and total floor area may also be used to determine whether properties are similar. For example, information on the region (or address) may be used. Because climates differ depending on the region (for example, Okinawa and Hokkaido), buildings with the same building use, the same floor area classification, and the same region may be extracted as similar properties. Other information such as the number of floors of the building (high-rise building, medium-sized building, small building) may also be used.

[0058] The energy saving effect estimation system 100 records the average value of the extracted building data as the similar property average 40. For example, suppose two properties, building X1 data 310 and building XN data 320, are extracted as similar properties. In this case, the average value of each value of the normalized power receiving amount 311 and the corresponding value of the normalized power receiving amount 321 is calculated and set as the normalized power receiving amount 41.

[0059] For example, if the amount of power received by building X1 from 0:00 to 0:30 in August is 30 and the amount of power received by building XN from 0:00 to 0:30 in August is 32, the value of normalized power received amount 41 for August from 0:00 to 0:30 will be 31 (= (30 + 32) / 2). Note that it is also possible to select one property with the highest similarity and set that value as the normalized power received amount 41 and the normalized air conditioning amount 42.

[0060] The energy saving effect estimation system 100 calculates a model 70 based on the similar property average 40 and the contracted power 51. The model 70 of the received power amount 71 is generated from time series data obtained by multiplying each value of the normalized received power amount 41 by the contracted power 51. The model 70 of the air conditioning power amount 72 is generated from time series data obtained by multiplying each value of the normalized air conditioning amount 42 by the corresponding model value of the received power amount (the value of the received power amount 71).

[0061] Specifically, the energy saving effect estimation system 100 sets the value obtained by multiplying each value of the normalized amount of received power 41 by the contracted power 51 as the amount of received power 71. The reason for doing so is that, since "normalized amount of received power = amount of received power / annual maximum value of amount of received power", the intention is to obtain a value equivalent to "amount of received power" by multiplying "normalized amount of received power" by "contracted power", which is a value equivalent to "annual maximum value of amount of received power".

[0062] Furthermore, the energy saving effect estimation system 100 sets the value obtained by multiplying each value of the normalized air conditioning amount 42 by the value of the corresponding received power amount 71 as the air conditioning power amount 72. The reason for doing this is that, since "normalized air conditioning amount = received air conditioning amount / received power amount", the intention is to obtain a value equivalent to "air conditioning power amount" by multiplying "normalized air conditioning amount" by "received power amount".

[0063] The energy saving effect estimation system 100 uses the obtained model 70 and calculation conditions 60 to calculate an estimated calculation result 80 as the reduction effect due to energy saving control. The calculation method of the estimated calculation result 80 is the same as the calculation method explained using Fig. 1. In Fig. 1, the received power amount 12 and the air conditioning power amount 13 of the target building data 10, which are actual measured values, are used, but in Fig. 4, the estimated received power amount 71 and the air conditioning power amount 72 of the model 70 are used instead of the actual measured values ​​to obtain the estimated calculation result 80.

[0064] The following describes the processing executed by the energy-saving effect estimation system 100 with reference to a flowchart.

[0065] In S101, the energy saving effect estimation system 100 acquires from the model calculation data 200 a plurality of building data stored in the storage device 105 (building X1 data 210 of building X1 to building XN data 220 of building XN in the model calculation data 200).

[0066] In S102, the energy saving effect estimation system 100 normalizes each of the above building data (normalized air conditioning amount = air conditioning power amount / received power amount, normalized received power amount = received power amount / annual maximum value of received power amount) and stores it as model calculation data 300.

[0067] In S103, the energy saving effect estimation system 100 extracts building data having building information similar to that of the target building data 50 (matching building use and similar total floor area) from the plurality of building data in the model calculation data 300. In S104, the energy saving effect estimation system 100 records the average value of the extracted building data as the similar property average 40.

[0068] In S105, the energy saving effect estimation system 100 generates a model 70 based on the similar property average 40 and the contracted power 51 of the target building data 50 (received power amount = normalized received power amount × contracted power, air conditioning power amount = normalized air conditioning amount × received power amount).

[0069] In S106, the energy saving effect estimation system 100 sets the reduction rate of the air conditioning power amount from the energy saving control setting 61 and the reduction rate 62 for each energy saving setting. For example, the user may set the energy saving control by input from a terminal device. If the user sets "indoor unit air flow control (strong)" from the terminal, the energy saving control setting 61 is set to "indoor unit air flow control (strong)". In this case, the reduction rate of the air conditioning power amount is set to 30% (see FIG. 2). The data of the target building data 50 may also be set by the user by input from the terminal.

[0070] In S107, the energy saving effect estimation system 100 calculates the power reduction effect 81 and the peak power reduction effect 82 in the estimated calculation result 80 from the model 70 and the reduction rate of air conditioning power (for example, the reduction rate of the annual received power amount of the target building, the reduction rate of peak power of the target building, etc.).

[0071] As described above, the energy-saving effect estimation system 100 is a system that estimates the effect of reducing power consumption in a target building (target building). The energy-saving effect estimation system 100 includes a storage device 105 and a CPU 101 as a processing device. The storage device 105 stores characteristic data (contracted power 51, building information 52) that is data related to the power consumption of the target building, and building data (building X1 data 210 to building XN data 220) for each of multiple buildings (buildings X1 to XN) different from the target building. The CPU 101 estimates the effect of reducing power consumption in the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls the air conditioning equipment in an energy-saving manner. The building data for each of the multiple buildings includes the amount of power received by the building (received power amounts 211 to 221) and the amount of air conditioning power consumed by the air conditioning equipment out of the amount of power received by the building (air conditioning power amounts 212 to 222). The CPU 101 generates normalized data (normalized received power amounts 311-321, normalized air conditioning amounts 312-322) by normalizing the received power amounts and air conditioning power amounts in a plurality of buildings. The CPU 101 uses the feature data to extract similar buildings from the plurality of buildings that have similar power consumption trends to the target building. The CPU 101 uses the normalized data of the similar buildings and the feature data to generate a model 70 of the received power amount 71 and the air conditioning power amount 72 of the target building. The CPU 101 inputs the reduction rate of air conditioning power amount due to energy saving control (calculation condition 60) into the model 70 to estimate the reduction effect (estimated calculation result 80).

[0072] In this way, even if historical data on the air conditioning energy consumption of a target building is not available, by normalizing the historical data on the received power consumption and air conditioning energy consumption of multiple buildings other than the target building, this historical data can be used to estimate the energy consumption of the target building. Furthermore, by using feature data related to the target building's power consumption, similar buildings with similar energy consumption trends to the target building can be extracted without adding dedicated equipment to measure the target building's historical data, and a model of the received power consumption and air conditioning energy consumption of the target building can be obtained. Because the reduction effect is estimated based on the historical data on the air conditioning energy consumption of similar buildings, the reduction effect on the building's energy consumption due to energy-saving control of the air conditioning equipment can be accurately estimated. This makes it possible to accurately estimate the reduction effect on the target building's energy consumption when the air conditioning equipment is controlled in an energy-saving manner, without adding dedicated equipment.

[0073] The received power amount and the air conditioning power amount are both time-series data showing the transition of the amount of power per unit time. The normalized data includes normalized received power amounts (normalized received power amounts 311-321) obtained by normalizing the received power amounts (received power amounts 211-221) and normalized air conditioning amounts (normalized air conditioning amounts 312-322) obtained by normalizing the air conditioning power amounts (air conditioning power amounts 212-222). The normalized received power amounts are time-series data obtained by dividing each value of the received power amount by the annual maximum value of the received power amount (maximum received power amount). The normalized air conditioning amounts are time-series data obtained by dividing each value of the air conditioning power amount by the corresponding value of the received power amount. In this way, by normalizing based on the ratio between the received power amount and the maximum received power amount and the ratio between the air conditioning power amount and the received power amount, it is possible to accurately estimate the reduction effect of the power consumption of the target building when the air conditioning equipment is controlled in an energy-saving manner.

[0074] The feature data includes the contracted power 51 of the target building. A model 70 of the received power amount 71 is generated from time series data obtained by multiplying each value of the normalized received power amount by the contracted power. A model 70 of the air conditioning power amount 72 is generated from time series data obtained by multiplying each value of the normalized air conditioning amount by the value of the model 70 of the corresponding received power amount 71. In this way, by obtaining a model 70 from the normalized data using the contracted power 51 (feature data) of the target building, it is possible to accurately estimate the effect of reducing the power consumption of the target building when the air conditioning equipment is controlled in an energy-saving manner.

[0075] The feature data includes the use and total floor area of ​​the target building (building information 52). The building data (building X1 data 210 to building XN data 220) of each of the multiple buildings (buildings X1 to XN) includes the building's use and total floor area (building information 313 to building information 323). The CPU 101 extracts, from the multiple buildings, buildings whose use matches that of the target building and whose difference in total floor area from the target building is within a predetermined range as similar buildings. In this way, by extracting similar properties using the building's use and total floor area (feature data), it is possible to accurately estimate the reduction in power consumption of the target building when the air conditioning equipment is controlled in an energy-saving manner.

[0076] [Second embodiment] The energy saving effect estimation system 100 according to the first embodiment has building information 52 of target building data 50. Then, using the building information 52, similar properties to the target building are extracted from a plurality of property data, and a model 70 of the target building is estimated based on the extracted similar properties.

[0077] On the other hand, the energy saving effect estimation system 100a according to the second embodiment has monthly power consumption 57 in the target building data 55. This monthly power consumption 57 is the actual power consumption for each month obtained from bills from the power company, etc. Then, a model 70 of the target building is estimated from information on multiple properties using the power consumption 57. Details will be explained below using FIG. 9. Note that explanations of parts that are the same as those in the first embodiment will be omitted as a general rule.

[0078] 9 is a diagram for explaining the flow of data processing in the energy saving effect estimation system 100a according to the second embodiment. The energy saving effect estimation system 100a is also a system that estimates the effect of reducing the amount of power consumption in a target building.

[0079] The energy-saving effect estimation system 100a stores characteristic data (contracted power 56, data string showing monthly changes in the amount of power consumed in the target building over the course of a year (monthly power consumption 57)) that is data related to the amount of power received by the target building, and building data (building X1 data 260 to building XN data 270) for each of a plurality of buildings (buildings X1 to XN) that are different from the target building. The energy-saving effect estimation system 100a estimates the reduction effect of the amount of power received by the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls the air conditioning equipment in an energy-saving manner. The building data for each of the plurality of buildings includes the amount of power received by the building (received power amounts 261 to 271) and the amount of air conditioning power consumed by the air conditioning equipment out of the amount of power received by the building (air conditioning power amounts 262 to 272). The energy saving effect estimation system 100a generates normalized data (normalized received power amounts 361-371, normalized air conditioning amounts 362-372) by normalizing the amount of received power and the amount of air conditioning power in a plurality of buildings. The energy saving effect estimation system 100a inputs the reduction rate of air conditioning power amount due to energy saving control (calculation condition 60) into a model 70, thereby estimating the reduction effect (estimated calculation result 80).

[0080] The following description will be made with reference to Fig. 9. The storage device 105 of the energy saving effect estimation system 100a stores target building data 55, model calculation data 250, 350, 450, model 70, calculation conditions 60, and trial calculation results 80.

[0081] The model calculation data 250 is data that records building data of multiple buildings different from the target building. Here, the multiple buildings are N buildings: building X1, building X2, ..., building XN. The model calculation data 250 includes building X1 data 260 of building X1 to building XN data 270 of building XN.

[0082] Building X1 data 260 includes received power amount 261 and air conditioning power amount 262 of building X1. Building XN data 270 includes received power amount 271 and air conditioning power amount 272 of building XN. The received power amount 261, air conditioning power amount 262, etc. are data in the same format as the received power amount 12 and air conditioning power amount 13 described in FIG. 1, and are 12 months' worth of data as shown in graph 21 in FIG. 3.

[0083] The energy saving effect estimation system 100a normalizes each building data of the model calculation data 250 to calculate model calculation data 350 (normalized data). "Normalized received power amount = received power amount / annual maximum value of received power amount" and "normalized air conditioning amount = air conditioning power amount / received power amount", and in this respect, this is the same as the calculation method for the model calculation data 300 in FIG. 4.

[0084] Next, the energy saving effect estimation system 100a generates conversion data (received power amounts 461-471, air conditioning power amounts 462-472) from the normalized data (normalized received power amounts 361-371, normalized air conditioning amounts 362-372).

[0085] Specifically, the conversion data of the amount of received power (received power amounts 461 to 471) is time series data obtained by multiplying each value of the normalized amount of received power (normalized amount of received power 361 to 371) by the contracted power 56. The conversion data of the amount of air conditioning power (air conditioning power amounts 462 to 472) is time series data obtained by multiplying each value of the normalized air conditioning amounts (normalized air conditioning amounts 362 to 372) by the value of the corresponding conversion data of the amount of received power (received power amounts 461 to 471).

[0086] For example, each value of the normalized received power amount 361 of the building X1 data 360 is multiplied by the contracted power 56 of the target building, and the result is set as the received power amount 461 of the building X1 data 460. Each value of the normalized air conditioning amount 362 of the building X1 data 360 is multiplied by the corresponding value of the received power amount 461, and the result is set as the air conditioning power amount 462 of the building X1 data 460.

[0087] Next, the energy saving effect estimation system 100a extracts similar buildings (similar properties) and generates a model 70 from the converted data of the similar buildings. The feature data of the second embodiment differs from the feature data of the first embodiment. The feature data of the second embodiment includes a contracted power 56 of the target building and a data string (monthly power consumption 57) indicating the change in the amount of power consumed by the target building on a monthly basis over a year.

[0088] The energy saving effect estimation system 100a extracts as similar buildings from among a plurality of buildings (building X1 data 460 to building XN data 470) buildings whose data string showing the monthly change in the amount of electricity consumed in the target building over a year (monthly power consumption 57: 12 power consumption amounts from January to December) is similar to the data string showing the monthly change in the amount of electricity consumed over a year calculated from the converted data.

[0089] First, the monthly power consumption of each of the building X1 data 460 to building XN data 470 is calculated using the method described above. Whether the monthly power consumption (12 power consumption amounts from January to December) is similar may be determined, for example, by whether the determination formula "absolute value of (power consumption of the target building in January - power consumption of building X1 in January) + absolute value of (power consumption of the target building in February - power consumption of building X1 in February) + ··· + absolute value of (power consumption of the target building in December - power consumption of building X1 in December)) < predetermined value" is satisfied. If the result is less than the predetermined value, the monthly power consumption of the target building and building X1 is similar.

[0090] Or, the judgment formula is "((Power consumption of the target building in January - Power consumption of building X1 in January) 2 + (Power consumption of target building in February - Power consumption of building X1 in February) 2 +···+(Power consumption of target building in December - Power consumption of building X1 in December) 2 ) 1 / 2 The judgment may be made based on whether the value satisfies a "predetermined value." Furthermore, weighting may be performed according to the month or season in the similarity judgment. For example, if each item corresponding to each month in the judgment formula is multiplied by a weighting coefficient, and summer and winter are to be emphasized, the weighting coefficients for the months corresponding to summer and winter in the judgment formula can be set large.

[0091] The energy saving effect estimation system 100a records the average value of the extracted building data as the model 70. For example, suppose two properties, building X1 data 460 and building XN data 470, are extracted as similar properties. In this case, the average value of each value of received power energy 461 and the corresponding value of received power energy 471 is taken, and this is set as received power energy 71. The average value of each value of air conditioning power energy 462 and the corresponding value of air conditioning power energy 472 is taken, and this is set as air conditioning power energy 72. It is also possible to select one property with the highest similarity (the property with the smallest evaluation value according to the judgment formula), and set this value as received power energy 71 and air conditioning power energy 72.

[0092] The energy saving effect estimation system 100a calculates an estimated calculation result 80 as the reduction effect due to the energy saving control, using the obtained model 70 and the calculation conditions 60. The calculation method of the estimated calculation result 80 is the same as the calculation method described with reference to FIG.

[0093] The process executed by the energy-saving effect estimation system 100a will be described below with reference to a flowchart. Fig. 10 is a flowchart of the process executed by the energy-saving effect estimation system 100a.

[0094] In S201, the energy saving effect estimation system 100a acquires a plurality of building data (building X1 data 260 of building X1 to building XN data 270 of building XN in the model calculation data 250) stored in the storage device 105. In S202, the energy saving effect estimation system 100a normalizes the building data to generate normalized data (normalized received power amount=received power amount / annual maximum value of received power amount, normalized air conditioning amount=air conditioning power amount / received power amount), and stores the normalized data as model calculation data 350.

[0095] In S203, the energy saving effect estimation system 100a generates converted data from the normalized data (received power amount = normalized received power amount × contracted power, received air conditioning amount = normalized air conditioning amount × received power amount), and stores the converted data as model calculation data 450. In S204, the energy saving effect estimation system 100a extracts, from the multiple properties in the model calculation data 450, properties whose monthly power consumption is similar to the monthly power consumption amount 57 of the target building data 55. In S205, the energy saving effect estimation system 100a records the average value of the extracted building data as the model 70.

[0096] In S206, the energy saving effect estimation system 100a sets the air conditioning power reduction rate from the energy saving control settings 61 and the reduction rate 62 for each energy saving setting. For example, the energy saving control may be set by a user inputting it into a terminal. In S207, the energy saving effect estimation system 100a calculates the power reduction effect 81 and the peak power reduction effect 82 in the estimated calculation result 80 from the model 70 and the air conditioning power reduction rate.

[0097] As described above, CPU 101 generates normalized data (normalized received power amounts 361-371, normalized air conditioning amounts 362-372) by normalizing the received power amounts and air conditioning power amounts in multiple buildings. The feature data includes contracted power 56 of the target building and a data string (monthly power consumption amount 57) showing the trend in the monthly power amount of the target building over the course of a year. CPU 101 generates converted data (received power amounts 461-471, air conditioning power amounts 462-472) from the normalized data (normalized received power amounts 361-371, normalized air conditioning amounts 362-372). The converted data of received power amounts (received power amounts 461-471) is time-series data obtained by multiplying each value of the normalized received power amounts (normalized received power amounts 361-371) by the contracted power amount 56. The conversion data for air conditioning power consumption (air conditioning power consumption 462-472) is time-series data obtained by multiplying each value of the normalized air conditioning volume (normalized air conditioning volume 362-372) by the corresponding value of the conversion data for received power consumption (received power consumption 461-471). CPU 101 extracts, from among multiple buildings, buildings whose data string (monthly power consumption 57) showing the monthly change in power consumption over a year for the target building is similar to the data string showing the monthly change in power consumption over a year calculated from the conversion data, as similar buildings. CPU 101 generates model 70 from the conversion data for similar buildings. CPU 101 inputs the reduction rate of air conditioning power consumption due to energy saving control (calculation condition 60) into model 70 to estimate the reduction effect (estimated calculation result 80).

[0098] In this way, even if historical data on the target building's air conditioning energy consumption is not available, by normalizing historical data on the received power consumption and air conditioning energy consumption of multiple buildings other than the target building, this historical data can be used to estimate the target building's energy consumption. Furthermore, by using the target building's contracted power consumption 51 and monthly power consumption 57 as characteristic data, similar buildings with similar energy consumption trends to the target building can be extracted, and a model of the target building's received power consumption and air conditioning energy consumption can be obtained. Because the reduction effect is estimated based on the historical data on the air conditioning energy consumption of similar buildings, the reduction effect on the building's energy consumption due to energy-saving control of the air conditioning equipment can be accurately estimated. This makes it possible to accurately estimate the reduction effect on the target building's energy consumption when the air conditioning equipment is controlled in an energy-saving manner, without adding dedicated equipment.

[0099] [Note] The above-described embodiment is a specific example of the following additional notes.

[0100] (Appendix 1) An energy saving effect estimation system that estimates the reduction effect of power consumption of a target building, a storage device that stores characteristic data related to the amount of power consumption of the target building and building data of each of a plurality of buildings different from the target building; a processing device that estimates an effect of reducing the amount of power consumed by the target building when the air conditioning equipment of the target building is operated under energy-saving control, the building data for each of the plurality of buildings includes the amount of power consumption of the building and the amount of air conditioning power consumed by an air conditioning facility within the amount of power consumption of the building; The processing device includes: generating normalized data by normalizing the amounts of power consumption and air conditioning power consumption in the plurality of buildings; extracting, from the plurality of buildings, similar buildings having a similar tendency in power consumption to the target building using the feature data; generating a model of the power consumption and air-conditioning power consumption of the target building using the normalized data and the feature data of the similar building; an energy saving effect estimation system that estimates the reduction effect by inputting a reduction rate of the amount of air conditioning power consumption due to the energy saving control into the model;

[0101] (Appendix 2) The power consumption amount and the air conditioning power amount are both time-series data showing a change in the amount of power per unit time, the normalized data includes a normalized consumption amount obtained by normalizing the power consumption amount and a normalized air conditioning amount obtained by normalizing the air conditioning power amount, the normalized consumption is time-series data obtained by dividing each value of the power consumption by the annual maximum value of the power consumption, 2. The energy-saving effect estimation system according to claim 1, wherein the normalized air-conditioning amount is time-series data obtained by dividing each value of the air-conditioning power amount by the corresponding value of the power consumption amount.

[0102] (Appendix 3) The characteristic data includes a contract power of the target building, the power consumption model is generated from time series data obtained by multiplying each value of the normalized power consumption by the contracted power; 3. The energy-saving effect estimation system according to claim 1, wherein the model of the air-conditioning power amount is generated from time-series data obtained by multiplying each value of the normalized air-conditioning amount by the corresponding value of the model of the power consumption amount.

[0103] (Appendix 4) The characteristic data includes the purpose and total floor area of ​​the target building, The building data for each of the plurality of buildings includes a building use and a total floor area; The energy saving effect estimation system according to any one of appendices 1 to 3, wherein the processing device extracts, from among the plurality of buildings, a building whose use is the same as that of the target building and whose difference in total floor area with respect to the target building is within a predetermined range as the similar building.

[0104] (Appendix 5) The characteristic data includes a contracted power of the target building and a data string indicating a change in monthly power consumption of the target building over one year, The processing unit generates transformation data from the normalized data; the converted data of the power consumption is time-series data obtained by multiplying each value of the normalized power consumption by the contracted power; the conversion data of the air conditioning power amount is time-series data obtained by multiplying each value of the normalized air conditioning amount by a corresponding value of the conversion data of the power consumption, The processing device includes: extracting, from among the plurality of buildings, buildings having a data string similar to a data string showing a monthly change in the amount of electricity consumed over a one-year period of the target building and a data string showing a monthly change in the amount of electricity consumed over a one-year period calculated from the converted data, as the similar buildings; 3. The energy saving effect estimation system according to claim 2, wherein the model is generated from the conversion data of the similar building.

[0105] (Appendix 6) A control method for an energy saving effect estimation system that estimates a reduction effect of power consumption of a target building, comprising: the energy-saving effect estimation system includes a storage device that stores feature data related to the amount of power consumption of the target building and building data of each of a plurality of buildings different from the target building; The control method includes a step of estimating an effect of reducing power consumption of the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls the air conditioning equipment in an energy-saving manner; the building data for each of the plurality of buildings includes the amount of power consumption of the building and the amount of air conditioning power consumed by an air conditioning facility within the amount of power consumption of the building; The estimating step includes: generating normalized data by normalizing the amounts of power consumption and air conditioning power consumption in the plurality of buildings; extracting, from the plurality of buildings, similar buildings having a tendency of power consumption similar to that of the target building using the feature data; generating a model of the amount of power consumption and the amount of air conditioning power consumption of the target building using the normalized data and the feature data of the similar building; and estimating the reduction effect by inputting a reduction rate of the air conditioning power amount due to the energy saving control into the model.

[0106] The embodiments disclosed herein are merely examples and are not limited to the above. The scope of the present invention is defined by the claims, and it is intended to include all modifications within the meaning and scope of the claims. [Explanation of symbols]

[0107] 10 Target building data, 11 Contracted power, 12 Power received, 13 Air conditioning power, 20 Table, 21-23 Graph, 40 Average for similar properties, 41 Normalized power received, 42 Normalized air conditioning, 50 Target building data, 51 Contracted power, 52 Building information, 55 Target building data, 56 Contracted power, 57 Monthly power consumption, 60 Calculation conditions, 61 Energy saving control settings, 62 Reduction rate for each energy saving control, 70 Model, 71 Power received, 72 Air conditioning power, 80 Estimation results, 81 Power reduction effect, 82 Peak power reduction effect, 100, 100a Energy saving effect estimation system, 101 CPU, 102 RAM, 103 ROM, 104 I / F device, 105 Storage device, 106 Communication bus, 200, 300 Data for model calculation, 210, 310 Building X1 data, 220, 320 Building XN data, 211, 221 Power received, 212, 222 Air conditioning power, 213, 223 Building information, 311, 321 Normalized power received, 312, 322 Normalized air conditioning, 313, 323 Building information, 250, 350, 450 Data for model calculation, 260, 360, 460 Building X1 data, 270, 370, 470 Building XN data, 261, 271, 461, 471 Power received, 262, 272, 462, 472 Air conditioning power, 361, 371 Normalized power received, 362, 372 Normalized air conditioning.

Claims

1. An energy saving effect estimation system that estimates the reduction effect of power consumption of a target building, a storage device that stores characteristic data related to the amount of power consumption of the target building and building data of each of a plurality of buildings different from the target building; a processing device that estimates an effect of reducing the amount of power consumed by the target building when the air conditioning equipment of the target building is operated under energy-saving control, the building data for each of the plurality of buildings includes the amount of power consumption of the building and the amount of air conditioning power consumed by an air conditioning facility within the amount of power consumption of the building; The processing device includes: generating normalized data by normalizing the amounts of power consumption and air conditioning power consumption in the plurality of buildings; extracting, from the plurality of buildings, similar buildings having a similar tendency in power consumption to the target building using the feature data; generating a model of the power consumption and air-conditioning power consumption of the target building using the normalized data and the feature data of the similar building; an energy saving effect estimation system that estimates the reduction effect by inputting a reduction rate of the air conditioning power amount due to the energy saving control into the model;

2. The power consumption amount and the air conditioning power amount are both time-series data showing a change in the amount of power per unit time, the normalized data includes a normalized consumption amount obtained by normalizing the power consumption amount and a normalized air conditioning amount obtained by normalizing the air conditioning power amount, the normalized consumption is time-series data obtained by dividing each value of the power consumption by the annual maximum value of the power consumption, The energy-saving effect estimation system according to claim 1 , wherein the normalized air-conditioning amount is time-series data obtained by dividing each value of the air-conditioning power amount by the corresponding value of the power consumption amount.

3. The characteristic data includes a contract power of the target building, the power consumption model is generated from time series data obtained by multiplying each value of the normalized power consumption by the contracted power; The energy-saving effect estimation system according to claim 2 , wherein the model of the air-conditioning power amount is generated from time-series data obtained by multiplying each value of the normalized air-conditioning amount by a corresponding value of the model of the power consumption amount.

4. The characteristic data includes the purpose and total floor area of ​​the target building, The building data for each of the plurality of buildings includes a building use and a total floor area; The energy saving effect estimation system according to any one of claims 1 to 3, wherein the processing device extracts as the similar building a building from among the plurality of buildings that has the same use as the target building and whose difference in total floor area with the target building is within a predetermined range.

5. The characteristic data includes a contracted power of the target building and a data string indicating a change in the amount of power consumed by the target building on a monthly basis over a one-year period, The processing unit generates transformation data from the normalized data; the converted data of the power consumption is time-series data obtained by multiplying each value of the normalized power consumption by the contracted power; the conversion data of the air conditioning power amount is time-series data obtained by multiplying each value of the normalized air conditioning amount by the value of the corresponding conversion data of the power consumption amount, The processing device includes: extracting, from among the plurality of buildings, buildings having a data string similar to a data string showing a monthly change in the amount of electricity consumed over a one-year period of the target building and a data string calculated from the converted data showing a monthly change in the amount of electricity consumed over a one-year period as the similar buildings; The energy-saving effect estimation system according to claim 2 , wherein the model is generated from the conversion data of the similar building.

6. A control method for an energy saving effect estimation system that estimates a reduction effect of power consumption of a target building, comprising: the energy-saving effect estimation system includes a storage device that stores feature data related to the amount of power consumption of the target building and building data of each of a plurality of buildings different from the target building; The control method includes a step of estimating an effect of reducing power consumption of the target building when the air conditioning equipment of the target building is operated under energy-saving control that controls the air conditioning equipment in an energy-saving manner; the building data for each of the plurality of buildings includes the amount of power consumption of the building and the amount of air conditioning power consumed by an air conditioning facility within the amount of power consumption of the building; The estimating step includes: generating normalized data by normalizing the amounts of power consumption and air conditioning power consumption in the plurality of buildings; extracting, from the plurality of buildings, similar buildings having a tendency of power consumption similar to that of the target building using the feature data; generating a model of the amount of power consumption and the amount of air conditioning power consumption of the target building using the normalized data and the feature data of the similar building; and estimating the reduction effect by inputting a reduction rate of the air conditioning power amount due to the energy saving control into the model.

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