Prediction system, control system, prediction method, and program

Abstract

We provide a forecasting system that can predict the power consumption of multiple consumers. [Solution] The prediction system 10 includes an acquisition unit 54 that acquires first information regarding the power consumption of at least some of the customers 20a by collecting measurement data from power measuring devices installed in at least some of the customers 20a from communication devices installed in at least some of the customers 20a that have contracts with an electric power company; a prediction unit 55 that predicts the power consumption of the multiple customers 20 based on the acquired first information; and an output unit 56 that outputs prediction data showing the predicted power consumption of the multiple customers 20.

Description

Technical Field

[0001] The present invention relates to a method for predicting the power consumption of a plurality of customers, etc.

Background Art

[0002] In order to balance power demand (consumption) and supply (generation) in a power system, technologies related to demand response for adjusting the power consumption of customers in response to requests from power companies are known. Patent Document 1 discloses a demand response compatible power control system.

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0004] The present invention provides a prediction system or the like that can predict the power consumption of a plurality of customers.

Means for Solving the Problems

[0005] A prediction system according to an aspect of the present invention includes an acquisition unit that acquires first information regarding the power consumption of at least some of a plurality of customers who contract with a power company by collecting measurement data of a power measurement device provided in the at least some of the customers from a communication device provided in the at least some of the customers, a prediction unit that predicts the power consumption of the plurality of customers based on the acquired first information, and an output unit that outputs prediction data indicating the predicted power consumption of the plurality of customers.

[0006] A control system according to one aspect of the present invention includes a control unit that controls equipment installed at at least some of the customers based on the prediction data output by the prediction system.

[0007] A prediction method according to one aspect of the present invention is a prediction method performed by a computer system, and includes an acquisition step of acquiring first information regarding the power consumption of at least some of the consumers by collecting measurement data from a power measuring device installed at at least some of the consumers from a communication device installed at at least some of the consumers who have a contract with an electric utility company; a prediction step of predicting the power consumption of the plurality of consumers based on the acquired first information; and an output step of outputting prediction data indicating the predicted power consumption of the plurality of consumers.

[0008] A program according to one aspect of the present invention is a program for causing the computer system to execute the prediction method described above. [Effects of the Invention]

[0009] The prediction system of the present invention can predict the power consumption of multiple consumers. [Brief explanation of the drawing]

[0010] [Figure 1] Figure 1 is a block diagram showing the configuration of the prediction system according to the embodiment. [Figure 2] Figure 2 is a sequence diagram of Example 1 of the prediction operation of the prediction system according to the embodiment for multiple consumers' power consumption. [Figure 3] Figure 3 shows an example of historical data used to predict the amount of power consumed from the start time to the end time of a given time slot. [Figure 4] Figure 4 shows an example of historical data used to predict power consumption from a time after the start time to the end time of a target time slot. [Figure 5]Figure 5 is a flowchart of the process for determining whether or not to perform a prediction. [Figure 6] Figure 6 is a sequence diagram of Example 2 of the prediction operation of the prediction system according to the embodiment for the amount of electricity consumed by multiple consumers. [Modes for carrying out the invention]

[0011] The embodiments will be described in detail below with reference to the drawings. Note that the embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, arrangement positions and connection configurations of components, steps, and the order of steps shown in the following embodiments are examples only and are not intended to limit the present invention. Furthermore, components in the following embodiments that are not described in an independent claim will be described as optional components.

[0012] Please note that each figure is a schematic diagram and not necessarily a strictly accurate representation. Furthermore, in each figure, substantially identical components are denoted by the same reference numerals, and redundant explanations may be omitted or simplified.

[0013] In this specification, the power consumption of multiple customers refers to the power consumption of a group of multiple customers when considered as a single entity, and can be rephrased as the total power consumption of multiple customers, or the sum of the power consumption of multiple customers. The power consumption of some customers refers to the power consumption of a group of some customers when considered as a single entity, or in other words, the sum of the power consumption of some customers.

[0014] (Embodiment) [composition] First, the configuration of the prediction system according to the embodiment will be described. Figure 1 is a block diagram showing the configuration of the prediction system according to the embodiment.

[0015] The forecasting system 10 is a system that can provide electricity-related services to each of the multiple customers 20 that have contracts with retail electricity provider A and belong to the same region (area). The services include, for example, a Demand Response (DR) service that controls energy resources 22 during periods of high electricity demand to suppress the power consumption of energy resources 22 (power received from the grid) and pays incentives to the customers 20 in return. Retail electricity provider A is, for example, a so-called deemed retail electricity provider or a new power company. Each of the multiple customers 20 may be a residential customer 20 or a non-residential building such as a factory 20. The total number of multiple customers 20 is, for example, several hundred to several hundred thousand.

[0016] When the second power management server 40 of retail electricity provider A implements the above-mentioned DR service based on the power consumption of multiple customers 20, it is necessary to receive actual power consumption data of the multiple customers 20 via Route C from the first power management server 30 of the general transmission and distribution company (major power company), which manages the power consumption of the multiple customers 20. However, there is generally a delay between when the actual data is measured and when the second power management server 40 can receive the actual data from the first power management server 30, and the problem is that the second power management server 40 (retail electricity provider A) cannot grasp the power consumption of multiple customers 20 in real time.

[0017] Therefore, in the prediction system 10, the prediction server 50 acquires the performance data (first information indicating power consumption) of the power consumption of a part of the plurality of customers 20a, i.e., the customers 20a, collected from the B route. The prediction server 50 predicts the power consumption of the plurality of customers 20 based on the acquired performance data and outputs the prediction data to the second power management server 40. Thereby, the prediction server 50 can assist the second power management server 40 (retail electricity business operator A) in grasping the power consumption of the plurality of customers 20 in real time. Since relatively recent performance data can be obtained from the B route, the prediction server 50 can provide highly accurate prediction data.

[0018] As shown in FIG. 1, the prediction system 10 includes an EMS (Energy Management System) controller 21 installed in each of the plurality of customers 20a, a plurality of energy resources 22, a first power measurement device 23, and a second power measurement device 24, a power measurement device 25 installed in each of the plurality of customers 20b, a first power management server 30, a second power management server 40, and a prediction server 50.

[0019] Note that both the customers 20a and the customers 20b are customers who have contracted with the retail electricity business operator A. However, the difference is that the customers 20a are customers who can provide the prediction server 50 with the performance data of the power consumption, while the customers 20b are customers who cannot provide the prediction server 5 with the performance data of the power consumption due to factors such as the specifications of the EMS controller installed in the customers 20b.

[0020] The EMS controller 21 is a device that manages the power consumption of the consumer 20 measured by the first power measurement device 23, the second power measurement device 24, or other power measurement devices (not shown) that have not undergone or submitted the verification specified by the measurement method (not available for measurement for billing). Note that the EMS controller 21 communicates with a plurality of energy resources 22, the first power measurement device 23, and the second power measurement device 24 via a local communication network. Note that the EMS controller 21 may not be provided, and the functions included in the EMS controller 21 may be included in the first power measurement device 23, the second power measurement device 24, or the energy resource 22.

[0021] The energy resource 22 is installed in a facility (such as a house) corresponding to the consumer 20. The energy resource 22 includes at least one piece of equipment such as a solar power generation system, a battery system, lighting equipment, air conditioning equipment, ventilation equipment, a heat pump type water heater, and a charging / discharging device for an electric vehicle.

[0022] The first power measurement device 23 is a so-called smart meter and is a specific measuring instrument (a measuring instrument that has undergone the verification specified by the measurement method). The first power measurement device 23 measures the power consumption (forward power flow) or reverse power flow in the consumer 20a and transmits the measured performance data to the first power management server 30 via Route A. Note that a concentrator or the like is not shown. Also, the first power measurement device 23 receives the performance data of the power consumption measured by the second power measurement device 24 via the IoT route and transmits the received performance data to the first power management server 30 via Route A. The first power measurement device 23 can also transmit the performance data indicating the power consumption to the EMS controller 21 via Route B.

[0023] The second power measuring device 24 corresponds to a special measuring instrument (a measuring instrument that has not undergone the verification prescribed by the Measurement Law). The second power measuring device 24 is, for example, a distribution board. The second power measuring device 24 measures the amount of electricity consumed at the customer 20a and transmits the measured actual data showing the amount of electricity consumed to the first power measuring device 23 via the IoT route. The second power measuring device 24 can also transmit the actual data showing the amount of electricity consumed to the EMS controller 21. The second power measuring device 24 may also be installed in an energy resource 22, such as a power conditioner or electric vehicle charging / discharging equipment, and may measure the amount of electricity generated by a solar power generation system, the amount of charge of an electric vehicle battery, and the amount of discharge of an electric vehicle battery.

[0024] The power measurement device 25 is a so-called smart meter and is a specified measuring instrument (a measuring instrument that has undergone verification as stipulated by the Measurement Law). The power measurement device 25 measures the amount of electricity consumed at customer 20b and transmits the actual data showing the measured amount of electricity consumed to the first power management server 30 via route A.

[0025] The first power management server 30 is a server used by a general power transmission and distribution company to manage actual power consumption data of multiple customers 20. The first power management server 30 is, for example, a HES (Head End System) or an MDMS (Meter Data Management System). The first power management server 30 receives actual power consumption data from multiple first power measuring devices 23 and multiple power measuring devices 25 via route A, and transmits the received actual data to the second power management server 40 via route C.

[0026] The second power management server 40 is a server used by retail electricity provider A to manage actual power consumption data for multiple customers 20. The second power management server 40 receives actual power consumption data from the first power management server 30 via route C. The second power management server 40 also receives predicted data from the prediction server 50, which shows the predicted power consumption of multiple customers 20.

[0027] The prediction server 50 predicts the power consumption of multiple customers 20 based on actual power consumption data of multiple customers 20a obtained via Route B, and outputs (transmits) the predicted data to the second power management server 40. The prediction server 50 is a server managed and operated by, for example, a company that manufactures and sells EMS controllers 21. The prediction server 50 comprises a communication unit 51, an information processing unit 52, and a storage unit 53. The prediction function in the prediction server 50 may be included in the EMS controller 21, in which case the EMS controller 21 may predict the power consumption of the customers 20a.

[0028] The communication unit 51 is a communication circuit for the prediction server 50 to communicate with the EMS controller 21 and the second power management server 40 via a wide-area communication network. The communication unit 51 may, for example, perform wired communication, but it may also perform wireless communication. There are no particular limitations on the communication standards used by the communication unit 51.

[0029] The information processing unit 52 performs information processing to predict the power consumption of multiple consumers 20. The information processing unit 52 is implemented by, for example, a microcomputer, but may also be implemented by a processor. The information processing unit 52 includes, as functional components, an acquisition unit 54, a prediction unit 55, an output unit 56, and a control unit 57. The functions of the acquisition unit 54, the prediction unit 55, the output unit 56, and the control unit 57 are realized, for example, by the microcomputer or processor constituting the information processing unit 52 executing a computer program stored in the storage unit 53.

[0030] The memory unit 53 is a storage device that stores information necessary to realize the above-mentioned information processing, such as computer programs executed by the information processing unit 52. The memory unit 53 is implemented, for example, by semiconductor memory.

[0031] [Example 1 of predictive operation for power consumption] Next, we will describe Example 1 of the prediction operation of the prediction server 50 for predicting the power consumption of multiple customers 20. Figure 2 is a sequence diagram of Example 1 of the prediction operation of the prediction system 10 for predicting the power consumption of multiple customers 20. As mentioned above, since multiple customers 20a correspond to a part of multiple customers 20, they are also referred to as some customers 20a to mean a part of multiple customers 20.

[0032] The acquisition unit 54 of the prediction server 50 communicates with the second power management server 40 using the communication unit 51 to acquire second actual data (an example of second information) of the power consumption of multiple consumers 20 from the second power management server 40 (S11). The second actual data is actual data provided from the first power management server 30 to the second power management server 40 via the C route, or in other words, actual data managed by the first power management server 30 of the general power transmission and distribution company. The second actual data is acquired, for example, by the acquisition unit 54 requesting it from the second power management server 40, but it may also be transmitted periodically from the second power management server 40. Note that the prediction server 50 does not have to acquire the second actual data in step S11.

[0033] Figure 3 shows the actual data acquired at the time of forecasting. In Figure 3, each rectangle represents a time slot. A time slot is, for example, a period of 30 minutes in length, and is the smallest unit of time used when controlling the energy resource 22. In other words, one day is divided into 48 time slots, each with a length of 30 minutes.

[0034] Figure 3(a) shows the second actual data of power consumption for multiple consumers 20, which is provided from the first power management server 30 to the second power management server 40 via route C. Hatched time slots indicate that there is second actual data at the current time, while white time slots without hatching indicate that there is no second actual data.

[0035] Next, the acquisition unit 54 uses the communication unit 51 to collect measurement data from multiple first power measuring devices 23 installed at some of the customers 20a from the EMS controllers 21 installed at some of the customers 20a (S12), thereby acquiring first actual data (an example of first information) of the power consumption of some of the customers 20a (S13). In other words, the acquisition unit 54 acquires first actual data of the power consumption of some of the customers 20a via route B. The first actual data is acquired, for example, by requesting it from the EMS controller 21, but it may also be transmitted periodically from the EMS controller 21.

[0036] Figure 3(b) shows the first actual data for the power consumption of some of the consumers 20a obtained via Route B. Hatched time slots indicate that there is first actual data at the current time, while white time slots without hatching indicate that there is no first actual data.

[0037] The prediction unit 55 predicts the power consumption of multiple consumers 20 based on the first actual data of the power consumption of some consumers 20a obtained in step S13 (S14).

[0038] In step S14, the prediction unit 55 predicts the amount of electricity consumed by some of the consumers 20a during the target time slot based on first actual data of the electricity consumption of some of the consumers 20a (Figure 3 (1)). The prediction unit 55 may predict the amount of electricity consumed during the target time slot by some of the consumers 20a using a time series forecasting method, or it may predict it using a machine learning model or the like. In the prediction, other data (features) such as weather forecast data may be used in addition to the first actual data of electricity consumption.

[0039] Next, the prediction unit 55 predicts the power consumption of multiple customers 20 based on the power consumption of some customers 20a (Figure 3 (2)). For example, the prediction unit 55 predicts the power consumption of multiple customers 20 by multiplying the power consumption of some customers 20a by the number of multiple customers 20 / the number of some customers 20a. In addition to the number of multiple customers 20 and the number of some customers 20a, the power consumption of multiple customers 20 may also be predicted by considering the base value (average value) of the power consumption of each of the multiple customers 20 and some customers 20a.

[0040] Next, the output unit 56 outputs (transmits) the predicted data showing the power consumption of the multiple consumers 20 predicted in step S14 to the second power management server 40 using the communication unit 51 (S15).

[0041] Through the operations described above, the prediction server 50 can assist the second power management server 40 (retail electricity provider A) in understanding the power consumption of multiple customers 20 in real time. In step S13 (route B), the first actual data is obtained relatively more recently than in step S11 (route C) (see Figure 3), so the prediction server 50 can provide highly accurate prediction data.

[0042] Furthermore, the output unit 56 of the prediction server 50 may, if necessary, output (transmit) the original first actual data (the first actual data acquired in step S13) of the prediction data to the second power management server 40 using the communication unit 51 (S16). This allows the second power management server 40 to verify the validity of the prediction data based on the original first actual data.

[0043] Furthermore, in step S14, the second actual data of power consumption of the multiple consumers 20 obtained in step S11 may also be used for prediction. For example, the prediction unit 55 predicts the amount of power consumption of the multiple consumers 20 in the target time slot based on the second actual data of power consumption of the multiple consumers 20 (Figure 3 (3)). The prediction unit 55 may perform the prediction using a time series forecasting method, or it may perform the prediction using a machine learning model or the like.

[0044] If the predicted data obtained by this prediction is used as the second predicted data, and the predicted data obtained from the first actual data of the power consumption of some consumers 20a (as described above) is used as the first predicted data, the prediction unit 55 will, for example, use the average value of the first predicted data and the second predicted data as the final predicted data. Alternatively, the prediction unit 55 may generate the final predicted data by correcting one of the first predicted data and the other. If the second predicted data is not used, the processing in step S11 can be omitted.

[0045] Furthermore, in Operation Example 1, the prediction unit 55 predicted the amount of power consumed from the start time to the end time of the target time slot (see Figure 3), but it can also predict the amount of power consumed from a time after the start time to the end time of the target time slot. Figure 4 shows an example of predicting the amount of power consumed from a time after the start time to the end time of the target time slot.

[0046] [Determining whether the prediction can be executed] The prediction unit 55 of the prediction server 50 may determine whether or not to perform a prediction. Figure 5 is a flowchart of the operation for determining whether or not to perform a prediction. Note that the operation in Figure 5 is performed repeatedly, starting with one time slot and then moving on to the next time slot.

[0047] The acquisition unit 54 of the prediction server 50 acquires first actual data of the power consumption of some of the consumers 20a (S21). The processing in step S21 is the same as in step S13.

[0048] Next, the prediction unit 55 determines whether or not to perform a prediction (S22). The prediction unit 55 determines whether or not to perform a prediction of the power consumption of multiple consumers 20 based on, for example, whether or not the ratio of the number of some consumers 20a from which first actual data could be obtained in step S21 to the total number of multiple consumers 20 exceeds a predetermined ratio. The predetermined ratio is determined empirically or experimentally as appropriate by the business operator or other entity that manages and operates the prediction server 50.

[0049] If the prediction unit 55 determines that the ratio of some customers 20a to the total number of customers 20 is less than or equal to a predetermined ratio (No in S22), it does not perform a prediction of the power consumption of the multiple customers 20. On the other hand, if the prediction unit 55 determines that the ratio of some customers 20a to the total number of customers 20 is greater than a predetermined ratio (Yes in S22), it performs a prediction of the power consumption of the multiple customers 20 (S23). The output unit 56 outputs (transmits) the prediction data showing the power consumption of the multiple customers 20 predicted in step S23 to the second power management server 40 using the communication unit 51 (S24). The processing in steps S23 and S24 is the same as in steps S14 and S15.

[0050] Thus, the prediction unit 55 may perform a prediction only when it is estimated that a relatively large number of consumers 20a from whom first actual data can be obtained can be acquired, and that highly reliable prediction data can be obtained. This allows the prediction server 50 to provide highly accurate prediction data to the second power management server 40.

[0051] If the prediction is not performed (No in S22), the output unit 56 may output (transmit) notification information to the second power management server 40 using the communication unit 51 to notify that the prediction will not be performed.

[0052] In Figure 5, an example is shown in which the forecasting unit 55 determines whether or not to perform the forecast based on the proportion of some customers 20a to the total number of customers 20, after obtaining first actual data on the power consumption of some customers 20a. However, the forecasting unit may also determine whether or not to perform the forecast based on the proportion of customers 20a to the total number of customers 20, regardless of whether or not the first actual data has been obtained.

[0053] [Example 2 of predictive operation for power consumption] In Operation Example 1, the prediction server 50 output prediction data to the second power management server 40. In this case, control of the energy resources 22 based on the prediction data is performed, for example, by the second power management server 40. However, the prediction server 50 may control the energy resources 22 installed in each of the consumers 20a based on the prediction data. Figure 6 is a sequence diagram of Example 2 of the prediction operation of the prediction system 10 for the power consumption of multiple consumers 20.

[0054] The processing in steps S11 to S14 is the same as in Example 1 of the power consumption prediction operation, so a detailed explanation is omitted. The output unit 56 outputs prediction data showing the power consumption of multiple consumers 20 predicted in step S14 (S15a), and the control unit 57 controls the energy resources 22 installed in each of some of the consumers 20a based on the output prediction data. Specifically, the control unit 57 sends a control command to the EMS controller 21 using the communication unit 51 (S17), causing the EMS controller 21 to control the energy resources 22 under the control of the EMS controller (S18).

[0055] For example, if the predicted power consumption shown by the forecast data is higher than the first threshold and it is considered that there will be an excess of electricity demand, the control unit 57 controls the energy resource 22 to reduce its power consumption in the target time slot. Specifically, the control unit 57 performs controls such as discharging the battery system (supplying power to other devices) or changing the set temperature of the air conditioning equipment. Also, if the predicted power consumption shown by the forecast data is lower than the second threshold and it is considered that there will be an excess of electricity supply, the control unit 57 controls the energy resource 22 to increase its power consumption. Specifically, the control unit 57 performs controls such as charging the electric vehicle using the charging / discharging device or starting the water heating process of the heat pump water heater earlier than scheduled.

[0056] In addition, although Figure 6 shows an example in which the control unit 57 spontaneously controls based on predicted data, the control unit 57 may also receive a control command from the second power management server 40 after step S16 in Figure 2 and perform control based at least on the received control command.

[0057] In this way, the prediction server 50 can control the energy resources 22 based on the prediction data.

[0058] [Differentiation] In the above embodiment, the first actual data of the power consumption of some consumers 20a acquired by the prediction server 50 (acquisition unit 54) was described as actual data measured by the first power measuring device 23. However, the first actual data may also be actual data measured by the second power measuring device 24, or actual data measured by other power measuring devices (not shown) that have not undergone the verification or notification stipulated in the Measurement Law (and therefore cannot be used for measurement for billing purposes).

[0059] In the above embodiment, the prediction server 50 obtained first actual data of power consumption from the EMS controller 21 installed at the customer 20a. However, it is not essential that the customer 20a has an EMS controller 21, and the prediction server 50 may obtain the first actual data from other energy resources 22 installed at the customer 20a. In other words, the prediction server 50 can obtain the first actual data from a communication device installed at the customer 20a.

[0060] In the above embodiment, an example was described in which first actual data of power consumption is obtained from some of the consumers 20a among the multiple consumers 20. However, the first actual data may be obtained from all of the multiple consumers 20. In other words, the first actual data only needs to be obtained from at least some of the consumers 20a among the multiple consumers 20. Note that "at least some of the consumers 20a" means at least one consumer 20a (one or more consumers 20a).

[0061] [Effects, etc.] The following describes examples of inventions that can be obtained from the disclosures in this specification, and explains the effects and other benefits that can be obtained from such inventions.

[0062] Invention 1 is a prediction system 10 comprising: an acquisition unit 54 that acquires first information regarding the power consumption of at least some of the consumers 20a by collecting measurement data from power measuring devices installed in at least some of the consumers 20a from communication devices installed in at least some of the consumers 20a that have contracts with an electric power company; a prediction unit 55 that predicts the power consumption of the multiple consumers 20 based on the acquired first information; and an output unit 56 that outputs prediction data showing the predicted power consumption of the multiple consumers 20. The first actual data in the above embodiment is an example of the first information.

[0063] Such a prediction system 10 can predict the power consumption of multiple consumers 20 based on first information obtained from communication devices installed in at least some of the consumers 20a.

[0064] Invention 2 is a prediction system 10 of Invention 1, wherein the prediction unit 55 performs a prediction of the power consumption of a plurality of consumers 20 based on the ratio of the number of at least some consumers 20a to the total number of consumers 20.

[0065] Such a forecasting system 10 can perform a forecast of the power consumption of multiple consumers 20 based on the ratio of the number of at least some of the consumers 20a to the total number of consumers 20.

[0066] Invention 3 is a prediction system 10 of Invention 2, wherein the prediction unit 55 performs a prediction of the power consumption of multiple consumers 20 when the ratio of the number of at least some consumers 20a to the total number of consumers 20 is greater than a predetermined ratio, and does not perform a prediction of the power consumption of multiple consumers 20 when the ratio of the number of at least some consumers 20a to the total number of consumers 20 is less than or equal to the predetermined ratio.

[0067] Such a forecasting system 10 can perform a forecast of the power consumption of multiple consumers 20, provided that the ratio of the number of at least some of the consumers 20a to the total number of consumers 20 is greater than a predetermined ratio.

[0068] Invention 4 is a prediction system 10 according to any of Inventions 1 to 3, wherein the acquisition unit 54 further acquires second information regarding the power consumption of multiple consumers 20 managed by the first power management server 30 of a general power transmission and distribution company, and the prediction unit 55 predicts the power consumption of the multiple consumers 20 based on the acquired first and second information. The second actual data in the above embodiment is an example of the second information.

[0069] Such a prediction system 10 can predict the power consumption of multiple consumers 20 based on first information as well as second information managed by the first power management server 30 of a general power transmission and distribution company.

[0070] Invention 5 is a prediction system 10 of any of Inventions 1 to 4, wherein the power measuring device is a specified meter (first power measuring device 23).

[0071] Such a prediction system 10 can predict the power consumption of multiple consumers 20 based on first information obtained from measurement data collected from specific measuring instruments.

[0072] Invention 6 is a prediction system 10 of any of Inventions 1 to 4, wherein the power measuring device is a special measuring instrument (second power measuring device 24).

[0073] Such a prediction system 10 can predict the power consumption of multiple consumers 20 based on first information obtained from measurement data collected from special measuring devices.

[0074] Invention 7 is a prediction system 10 of any of Inventions 1 to 6, wherein the communication device is an EMS controller 21.

[0075] Such a prediction system 10 can predict the power consumption of multiple consumers 20 based on first information obtained from EMS controllers 21 installed in at least some of the consumers 20a.

[0076] Invention 8 is a control system that includes a control unit that controls energy resources 22 provided at at least some consumers 20a based on prediction data output by any of the prediction systems 10 of Inventions 1 to 7. Such a control system is implemented by a prediction server 50 (prediction system 10 with a control unit 57) that performs Example 2 of the power consumption prediction operation, but it may also be implemented by a second power management server.

[0077] Such a control system can control energy resources 22 provided in at least some of the consumers 20 based on the predicted power consumption of multiple consumers 20, which is based on first information obtained from communication devices provided in at least some of the consumers 20a.

[0078] Invention 9 is a prediction method performed by a computer system, comprising: an acquisition step of acquiring first information regarding the power consumption of at least some of the consumers 20a by collecting measurement data from power measuring devices installed in at least some of the consumers 20a from communication devices installed in at least some of the consumers 20a among a plurality of consumers 20 contracted with an electric utility company; a prediction step of predicting the power consumption of the plurality of consumers 20 based on the acquired first information; and an output step of outputting prediction data indicating the predicted power consumption of the plurality of consumers 20.

[0079] This prediction method can predict the power consumption of multiple consumers 20 based on first information obtained from communication devices installed in at least some of the consumers 20a.

[0080] Invention 10 is a program for causing a computer system to execute the prediction method of Invention 9.

[0081] According to such a program, a computer can predict the power consumption of multiple consumers 20 based on first information obtained from communication devices installed in at least some of the consumers 20a.

[0082] (Other embodiments) Although embodiments have been described above, the present invention is not limited to the embodiments described above.

[0083] For example, in the above embodiment, the prediction system is implemented by multiple devices. In this case, the processing performed by the devices of the prediction system may be performed by other devices. Also, the prediction system may be implemented by a single device. Similarly, the control system may be implemented by multiple devices or by a single device.

[0084] Furthermore, in the above embodiment, the processing performed by a specific processing unit may be performed by another processing unit. Also, the order of multiple processing units may be changed, or multiple processing units may be executed in parallel.

[0085] Furthermore, in the above embodiment, each component may be realized by executing a software program suitable for each component. Each component may also be realized by a program execution unit such as a CPU or processor reading and executing a software program recorded on a recording medium such as a hard disk or semiconductor memory.

[0086] Furthermore, each component may be implemented by hardware. For example, each component may be a circuit (or integrated circuit). These circuits may form a single circuit as a whole, or they may be separate circuits. Also, each of these circuits may be a general-purpose circuit or a dedicated circuit.

[0087] Furthermore, general or specific embodiments of the present invention may be implemented as a system, apparatus, method, integrated circuit, computer program, or recording medium such as a computer-readable CD-ROM. Alternatively, they may be implemented as any combination of a system, apparatus, method, integrated circuit, computer program, and recording medium.

[0088] For example, the present invention may be implemented as a prediction system or prediction server according to the above embodiment. Furthermore, the present invention may be implemented as a method executed by a computer system such as a prediction system or prediction server, or as a program for causing a computer system to execute such a method. The present invention may also be implemented as a computer-readable non-temporary recording medium on which such a program is recorded.

[0089] Furthermore, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art could conceive, or forms realized by arbitrarily combining the components and functions of each embodiment without departing from the spirit of the present invention. [Explanation of Symbols]

[0090] 10 Prediction Systems 20, 20a, 20b Consumer 21 EMS Controller 22 Energy Resources 23. First power measuring device 24. Second Power Measuring Device 25 Power measuring device 30. First Power Management Server 40. Second Power Management Server 50 prediction servers 51 Communications Department 52 Information Processing Section 53 Memory section 54 Acquisition Department 55 Prediction Section 56 Output section 57 Control Unit

Claims

1. An acquisition unit that acquires first information regarding the power consumption of at least some of the customers by collecting measurement data from power measuring devices installed at at least some of the customers from communication devices installed at at least some of the customers who have a contract with an electric utility company, A prediction unit that predicts the amount of power consumption of the multiple consumers based on the acquired first information, It includes an output unit that outputs prediction data showing the predicted power consumption of the aforementioned plurality of consumers. Prediction system.

2. The prediction unit performs a prediction of the power consumption of the plurality of consumers based on the ratio of the number of at least some of the consumers to the total number of consumers. The prediction system according to claim 1.

3. The prediction unit, If the ratio of the number of at least some of the consumers to the total number of the multiple consumers is greater than a predetermined ratio, the power consumption of the multiple consumers is predicted. If the ratio of the number of at least some of the consumers to the total number of the multiple consumers is less than or equal to a predetermined ratio, the prediction of the power consumption of the multiple consumers will not be performed. The prediction system according to claim 2.

4. The acquisition unit further acquires second information regarding the power consumption of the multiple consumers managed by the management server of the general power transmission and distribution operator, The prediction unit predicts the amount of electricity consumed by the plurality of consumers based on the acquired first and second information. A prediction system according to any one of claims 1 to 3.

5. The aforementioned power measuring device is a specified measuring instrument. A prediction system according to any one of claims 1 to 3.

6. The aforementioned power measuring device is a special measuring instrument. A prediction system according to any one of claims 1 to 3.

7. The aforementioned communication device is an EMS (Energy Management System) controller. A prediction system according to any one of claims 1 to 3.

8. The system includes a control unit that controls equipment installed at at least some of the customers based on the prediction data output by the prediction system according to any one of claims 1 to 3. Control system.

9. A prediction method performed by a computer system, An acquisition step of obtaining first information regarding the power consumption of at least some of the customers by collecting measurement data from power measuring devices installed at at least some of the customers from communication devices installed at at least some of the customers who have a contract with an electric power company, A prediction step in which the amount of electricity consumed by the plurality of consumers is predicted based on the acquired first information, The step includes outputting forecast data that shows the predicted power consumption of the plurality of consumers. Prediction method.

10. A program for causing the computer system to execute the prediction method described in claim 9.

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