JP7706084B2 - Resource demand forecasting method, program, resource demand forecasting system, and distribution board - Google Patents

Description

The present disclosure relates to a resource demand forecasting method, a program, a resource demand forecasting system, and a distribution board. More specifically, the present disclosure relates to a resource demand forecasting method, a program, a resource demand forecasting system, and a distribution board that forecast demand for resources such as electricity, gas, and water.

As an example of a conventional method, the power supply planning method described in Patent Document 1 is exemplified. The conventional example described in Patent Document 1 creates a schedule for water heating operation so that the sum of the planned power supply for hot water storage for the water heating operation of the electric water heater and the planned power supply for the equipment is equal to or less than the upper limit power, and so that the electric water heater can store the target amount of hot water at a specified time.

JP 2016-156532 A

Incidentally, full retail liberalization was implemented in Japan as part of the electricity system reform in April 2016. In response to this, the types of electricity businesses were reviewed, and they are now broadly divided into three categories: "power generation businesses," "power transmission and distribution businesses," and "retail electricity businesses." Of these, those who carry out "retail electricity businesses" (retail electricity businesses) are required to forecast demand for the next day and for a specified time period (future) and procure electricity from those who carry out "power transmission and distribution businesses" (power transmission and distribution businesses). It is possible that liberalization will also be promoted in the future for resources other than electricity, such as gas and water.

The objective of this disclosure is to provide a resource demand forecasting method, program, resource demand forecasting system, and distribution board that can assist in the generation of demand plans for resources such as electricity, gas, and water.

A resource demand forecasting method according to an aspect of the present disclosure is used when a business operator supplying resources generates a resource demand plan representing future resource demand in a target consumer group including a plurality of consumers that use the resources. The resource demand forecasting method includes a step of measuring the consumption of the resources by the consumers using measuring instruments installed in some or all of the consumers included in the target consumer group, and predicting the resource demand for the next day in synchronization with the night of the current day, which is a period for generating the resource demand plan for the next day , in order to generate the resource demand plan for the next day from the measured data of the consumption of the current day.

A program according to one aspect of the present disclosure causes one or more processors to execute the resource demand forecasting method.

A resource demand forecasting system according to an aspect of the present disclosure includes a plurality of measuring devices disposed at each of a plurality of consumers to which resources are supplied from a business operator, and a server that collects measurement data obtained by measuring the consumption of the resources consumed by the plurality of consumers with the measuring devices. In order to generate a resource demand plan for the next day from the measurement data of the consumption amount for the day acquired from the plurality of measuring devices, the server predicts the resource demand for the next day in synchronization with the night of the day, which is a period for generating the resource demand plan for the next day .

A distribution board according to an aspect of the present disclosure is used in the resource demand forecasting system. The distribution board includes a main breaker, one or more branch breakers that are branch-connected to a load-side terminal of the main breaker, and the meter. The meter measures the power consumed by a load via the main breaker and the branch breakers as the resource.

The resource demand forecasting method, program, resource demand forecasting system, and distribution board disclosed herein have the effect of assisting in the generation of resource demand plans.

FIG. 1 is a system configuration diagram of a power demand forecasting system according to an embodiment of the present disclosure. FIG. 2 is a block diagram of the power demand forecasting system and a division board according to an embodiment of the present disclosure.

The following describes an embodiment of the present disclosure. However, the embodiment described below is merely one of the various embodiments of the present disclosure. In addition, the embodiment described below can be modified in various ways depending on the design, etc., as long as the object of the present disclosure can be achieved.

In the following embodiments, electricity is used as an example of a resource, but the present disclosure can also be applied to resources other than electricity, such as gas (city gas) and water.

(1) Overview of electricity trading in the electricity market In Japan, a portion of the electricity generated by power generation companies is traded in wholesale electricity exchanges such as the Japan Electric Power Exchange (JEPX). The wholesale electricity exchanges have two main markets: the day-ahead market (spot market) and the hour-ahead market (intraday market). The day-ahead market is a market in which transactions take place the day before the supply date when retail electricity companies supply (sell) electricity to consumers (customers). On the other hand, the hour-ahead market is a market in which transactions take place on the day (electricity supply date) when retail electricity companies supply electricity to consumers. In the day-ahead market and hour-ahead market, electricity is traded for each of 48 time slots, which are divided into 30-minute intervals throughout the day.

Retail electricity suppliers procure electricity from the day-ahead market and the hour-ahead market, and supply the procured electricity to consumers. Here, the trading price in the day-ahead market at the wholesale electricity exchange is determined by the supply and demand balance between the demand plan of the retail electricity supplier and the supply plan of the electricity transmission and distribution company. In other words, if the demand plan exceeds the supply plan, the trading price will rise, but if the demand plan falls short of the supply plan, the trading price will fall.

However, if the amount of electricity that a retail electricity supplier procures based on a demand plan is less than the actual demand, the retail electricity supplier must pay a penalty fee (imbalance fee) to the electricity transmission and distribution company according to the shortfall in actual demand. On the other hand, if the amount of electricity that a retail electricity supplier procures based on a demand plan is more than the actual demand, the excess over actual demand is provided by the retail electricity supplier to the electricity transmission and distribution company free of charge.

However, in order to reduce electricity procurement costs for electricity retailers and supply electricity to consumers at low cost, it is desirable to generate a demand plan that reduces the difference between the electricity retailer's demand plan (electricity demand plan) and the actual demand.

(2) Overview of resource demand forecasting method and resource demand forecasting system according to embodiment The resource demand forecasting method according to the embodiment is used when a business operator supplying resources (electricity) generates a resource demand plan indicating future resource demand for a target consumer group including multiple consumers who use the resource (electricity). The resource demand forecasting method according to the embodiment (hereinafter referred to as the power demand forecasting method) measures the amount of power consumed by consumers using meters installed in some or all of the consumers included in the target consumer group. The power demand forecasting method includes a step of forecasting future power demand from the measured data of consumption in order to generate a power demand plan.

The power demand forecasting method generates a power demand plan by predicting future power demand from measurement data obtained by measuring the amount of power consumed by some or all of the consumers included in the target consumer group using a meter, and can therefore assist in the generation of the power demand plan. Moreover, the power demand forecasting method predicts future power demand from power consumption data measured using a meter installed at the consumer, and can therefore reduce the difference between the generated power demand plan and the actual amount of power demand.

The resource demand forecasting system (hereinafter referred to as the power demand forecasting system) according to the embodiment is a system for realizing the above-mentioned power demand forecasting method. That is, the power demand forecasting system includes a plurality of meters installed at each of a plurality of consumers to which power is supplied from a business operator, and a server that collects measurement data obtained by the meters on the amount of power consumed by the plurality of consumers. The server predicts future power demand from the measurement data on consumption obtained from the plurality of meters in order to generate a power demand plan.

The electricity demand forecasting system generates an electricity demand plan by predicting future electricity demand from measurement data obtained by measuring the electricity consumption of some or all of the consumers included in the target consumer group using meters, and can therefore assist in the generation of an electricity demand plan. Moreover, the electricity demand forecasting system predicts future electricity demand from electricity consumption data measured by meters installed at the consumers, and can therefore reduce the difference between the generated electricity demand plan and the actual electricity demand.

(3) Details of the Electricity Demand Forecasting System (3-1) Configuration of the Electricity Demand Forecasting System As shown in FIG. 1, the electricity demand forecasting system X1 includes a server system 1 and a plurality of measurement systems 2. Each of the plurality of measurement systems 2 is disposed at a consumer that receives electricity from the electricity retailer (purchases electricity). Each of the plurality of measurement systems 2 is connected to a network NT1 such as the Internet. Note that, unless otherwise specified, the "consumer" in this embodiment does not mean a natural person or a corporation that has a contract with the electricity retailer, but means a "facility" such as a residence of a natural person that has a contract with the electricity retailer and an office of a corporation that has a contract with the electricity retailer.

(3-2) Configuration of the Server System The server system 1 is realized, for example, by a computer system managed by an electricity retailer. The server system 1 has a communication device 11 that communicates with each measurement system 2 via a network NT1, and a prediction device 10 that predicts future electricity demand from electricity consumption data of each consumer collected from each measurement system 2 by the communication device 11 (see FIG. 1). However, the entity that manages the server system 1 is not limited to the electricity retailer, and may be, for example, a management company that undertakes management operations and the like from the electricity retailer.

The communication device 11 is configured to be connected to the Internet (network NT1) via an optical line (optical fiber cable), for example, and to communicate with each measurement system 2 via the optical line. Furthermore, the communication device 11 is connected to the wholesale electricity trading management server system 9 via another network NT2. The network NT2 may be the Internet like the network NT1, but is preferably configured, for example, as a VPN (Virtual Private Network).

The prediction device 10 is configured as a computer system mainly composed of a CPU, memory, and external storage devices such as a solid state drive (SSD) and a hard disk drive (HDD). The prediction device 10 performs the prediction process and the generation process of the power demand plan described below by having the CPU execute a program stored in either the memory or the external storage device. The program executed by the CPU is recorded in advance in either the memory or the external storage device of the computer system. However, the program executed by the CPU may be provided by being recorded on a non-transitory recording medium such as a memory card, or may be provided via the networks NT1 and NT2.

The electricity demand forecasting system X1 transmits (uploads) the electricity demand plan generated by the forecasting device 10 to the wholesale electricity trading management server system 9 via the network NT2 using the communication device 11. The wholesale electricity trading management server system 9 is a server system managed by the wholesale electricity exchange. The wholesale electricity trading management server system 9 determines the electricity trading price for each of 48 time slots in 30-minute increments based on the electricity demand plan submitted by the retail electricity supplier and the supply plan submitted by the electricity transmission and distribution company, and allows electricity to be traded at the determined trading price.

(3-3) Configuration of the Measurement System The measurement system 2 is configured as an internal device of the distribution board 3 (hereinafter, abbreviated as distribution board 3) according to this embodiment (see FIG. 2). The distribution board 3 is installed in one or more consumers among a target consumer group including a plurality of consumers who have concluded an electricity purchase and sale contract with a retail electricity supplier. Note that, although a residential distribution board (residential board) is illustrated as an example of the distribution board 3, it may be a distribution board other than a residential board, such as a cabinet-type distribution board.

The distribution board 3 has one main breaker 30, multiple branch breakers 31, and a measurement system 2 (see FIG. 2). The main breaker 30 is composed of a three-pole earth leakage circuit breaker. The three terminals (input terminals) on the input side (power source side) of the main breaker 30 are electrically connected in a one-to-one relationship to the first voltage line, the second voltage line, and the neutral line in a single-phase three-wire distribution system. In addition, the three terminals (output terminals) on the output side (load side) of the main breaker 30 are electrically connected in a one-to-one relationship to three conductive bars (bus bars).

The branch breakers 31 are, for example, circuit breakers equipped with an overcurrent tripping device. The two input side (power supply side) terminals (input terminals) of each branch breaker 31 are electrically connected to one of the first conductive bar conducting to the first voltage line and the second conductive bar conducting to the second voltage line, and to the third conductive bar conducting to the neutral line, by a branch line. However, the two input terminals of one or more branch breakers 31 may be electrically connected to the first conductive bar and the second conductive bar, by a branch line. Thus, an AC voltage with an effective value of 100 V is supplied to the former branch breaker 31, and an AC voltage with an effective value of 200 V is supplied to the latter branch breaker 31.

The two output side (load side) terminals (output terminals) of each branch breaker 31 are electrically connected to the load 4 via electric wires for indoor wiring. However, the load 4 may be directly connected to the branch breaker 31 or may be connected to the branch breaker 31 via wiring devices such as an outlet and a ceiling hook body. The load 4 connected via wiring devices is, for example, electrical equipment such as a washing machine, refrigerator, television receiver, and air conditioner. Also, the load 4 connected directly is, for example, an electromagnetic cooking appliance and a bathroom dryer.

The measurement system 2 has a measuring instrument 20, a control unit 21, a communication unit 22, and a sensor unit 23.

The sensor unit 23 has multiple current sensors 230. One of the multiple current sensors 230 measures the current flowing through the input terminal of the main breaker 30. The remaining current sensors 230 each measure the current flowing through the corresponding branch breaker 31. Each current sensor 230 is preferably configured as a Rogowski coil that is made of an air-core coil and produces an output according to the magnitude of the current passing through the air-core coil.

The measuring instrument 20 can measure the power consumption of the entire consumer and that of each branch circuit using the magnitude of the current (load current) measured by the multiple current sensors 230 of the sensor unit 23, the input voltage of the main breaker 30, and the voltage (effective value 100V or 200V) of each branch circuit (each branch breaker 31).

The communication unit 22 has a network controller (integrated circuit) that complies with the standard of a wired LAN, such as Ethernet. The communication unit 22 is electrically connected to a router (not shown) via a LAN cable. The communication unit 22 is connected to the network NT1 through the router. The communication unit 22 communicates with the server system 1 via the network NT1. However, the communication unit 22 may have a specific low-power wireless module that complies with a specific low-power radio station in the 920 MHz band instead of a network controller that complies with the standard of a wired LAN. The communication unit 22 that has the specific low-power wireless module performs specific low-power wireless communication with a HEMS (Home Energy Management System) controller (not shown). The HEMS controller is electrically connected to the router via a LAN cable, and communicates with the server system 1 from the router via the network NT1.

The control unit 21 is composed of, for example, a microcontroller mainly composed of a CPU (Central Processing Unit) and a memory. The control unit 21 causes the CPU to execute a program stored in the memory, thereby causing the CPU to perform processing such as sending data on power consumption measured by the measuring device 20 (power consumption measurement data) from the communication unit 22 to the server system 1. The program executed by the CPU is recorded in advance in the memory of the microcontroller. However, the program executed by the CPU may be recorded on a non-transitory recording medium such as a memory card and provided, or may be provided via a telecommunication line such as the Internet. The measuring device 20, the control unit 21, and the communication unit 22 are operated by a power supply voltage (for example, a DC voltage of 12V to 3V DC) created from an AC voltage with an effective value of 100V output to the output terminal of the main breaker 30.

The measurement system 2 can also be replaced by an electricity meter (such as a smart meter, which has a communication function) installed by a retail electricity supplier at a consumer's premises.

(3-4) Operation of the Power Demand Forecasting System Next, the operation of the power demand forecasting system X1 will be described. In the power demand forecasting system X1, each of the multiple measurement systems 2 transmits measurement data of power consumption, for example, every 30 minutes, to the server system 1. In the server system 1, the communication device 11 receives the measurement data transmitted from each measurement system 2, and the communication device 11 transmits the measurement data to the prediction device 10.

The prediction device 10 stores the measurement data for each consumer received from the communication device 11 in an external storage device (not shown), such as a hard disk drive. The prediction device 10 predicts the power demand (power consumption) of each consumer for the next day to generate a power demand plan for the next day. When predicting the power demand for the next day, it is desirable for the prediction device 10 to refer to the measurement data of the past power consumption of each consumer. For example, if both the current day and the next day are weekdays, the prediction device 10 considers the power consumption for the next day to be the same as the power consumption for the current day, and determines a predicted value for the power consumption (power demand) of each consumer for the next day.

Here, it is preferable that the prediction device 10 predicts the power demand for the next day by taking into account not only the measurement data but also at least one additional piece of information among date and time information, environmental information, and consumer information regarding the consumer.

The prediction device 10 may, for example, receive date and time information from an NTP (Network Time Protocol) server and predict the power demand for the next day by taking into account the received date and time information. For example, the prediction device 10 may statistically process past measurement data of each consumer, and when a significant difference in power consumption by day of the week is found, predict the power demand taking into account the day of the week of the next day.

The prediction device 10 may also obtain meteorological information (weather [sunny, cloudy, rainy, snowy, etc.], precipitation, temperature, wind speed, etc.) provided by the Japan Meteorological Agency or a licensed forecasting business operator via the Internet, and predict the power demand for the next day by taking into account the obtained meteorological information (environmental information). For example, the prediction device 10 may statistically process the past measurement data of each consumer, and predict the power demand taking into account the meteorological information when a significant difference is found between the meteorological information such as weather, precipitation, temperature, and wind speed and the power consumption.

Furthermore, the prediction device 10 may, for example, acquire customer information (consumer information) held by a retail electricity supplier, and predict the power demand for the next day by taking into account the acquired customer information (consumer information). Here, the customer information (consumer information) preferably includes, for example, information about the residents of the residence (number of people, gender, age, occupation, address, etc.) when the consumer is a residence. The prediction device 10 may, by statistically processing the past measurement data of each consumer, predict the power demand by taking into account the customer information (consumer information) when a significant difference in power consumption is recognized for the customer information (consumer information).

However, the prediction device 10 may also predict the power demand for the next day by taking into account at least two or more additional pieces of information from among date and time information, weather information, and consumer information (customer information). In this way, the prediction device 10 can improve the accuracy of the prediction by predicting the power demand by taking into account multiple pieces of additional information.

The prediction device 10 predicts the power demand for each consumer in 48 time slots, which are obtained by dividing the 24 hours from midnight of the next day to midnight of the day after that into 30-minute intervals. The prediction device 10 then generates a power demand plan for each of the 48 time slots for the next day by adding up the predicted values of power demand for each consumer for each of the 48 time slots. The prediction device 10 transmits the generated power demand plan from the communication device 11 to the wholesale power trading management server system 9 via the network NT2. It is preferable that the prediction device 10 performs power demand prediction in synchronization with the period for generating the power demand plan for the next day, for example, the period between 9:00 p.m. and 11:00 p.m. on the current day.

Incidentally, the prediction device 10 may predict the power demand using machine learning. The prediction device 10 uses the learned model to input the measurement data and additional information acquired from each measurement system 2 and predicts the power demand for the next day. The learned model is generated by machine learning using the measurement data and additional information acquired from each measurement system 2 as training data. An example of the machine learning algorithm is XGB (eXtreme Gradient Boosting) regression. However, the machine learning algorithm is not limited to XGB regression, and may be a neural network, a random forest, a decision tree, a logistic regression, a support vector machine (SVM), a Naive Bayes classifier, or k-nearest neighbors. Furthermore, the machine learning algorithm may be a Gaussian Mixture Model (GMM), k-means clustering, or the like. Furthermore, the machine learning learning method used by the prediction device 10 may be any of supervised learning, unsupervised learning, and reinforcement learning.

Here, assume that multiple electricity retailers have introduced the electricity demand forecasting system X1 according to this embodiment. In this case, for example, there is a possibility that the measurement system 2 of a consumer who receives electricity supply from one electricity retailer (Company A) may erroneously transmit measurement data to the server system 1 of another electricity retailer (Company B). If the server system 1 of the electricity retailer (Company B) then predicts electricity demand based on the erroneously transmitted measurement data, there is a risk that the accuracy of the prediction will decrease.

Therefore, in order to prevent erroneous transmission of measurement data as described above, it is preferable that in the power demand prediction system X1, the server system 1 performs a process (authentication process) to authenticate the measurement system 2. For example, the communication device 11 of the server system 1 can authenticate the measurement system 2 by checking whether the source address of a frame received from the communication unit 22 of the measurement system 2 matches a unique address of the measurement system 2 stored in advance in a memory or the like. Then, the prediction device 10 of the server system 1 can improve the prediction accuracy by using only the measurement data received from the authenticated measurement system 2 to predict power demand.

(4) Details of the power demand forecasting method In the power demand forecasting method, the power consumption of each consumer is measured by a meter installed at some or all of the consumers included in a plurality of consumers (hereinafter referred to as a target consumer group) that have concluded a power purchase contract with the retail electricity supplier. The power demand forecasting method includes a step of predicting future (e.g., the next day) power demand in order to generate a future (e.g., the next day) power demand plan. Here, the meter installed at each consumer is not limited to the meter 20 of the measurement system 2 of the power demand forecasting system X1. In the power demand forecasting method, for example, a watt-hour meter (smart meter) installed at each consumer by the retail electricity supplier can be used as a meter.

In the electricity demand forecasting method, the entity that forecasts future (e.g., the next day) electricity demand is a server system 1 (particularly the forecasting device 10) managed by an electricity retailer. The server system 1 can be used as a computer system that, for example, collects measurement data on power consumption from an electricity meter (smart meter) installed at each consumer, and calculates the electricity bill for each consumer from the collected measurement data. In this case, the entity that implements the electricity demand forecasting method is a program (the program according to this embodiment) that causes the processor (CPU) of the computer system (server system 1) to perform the process of forecasting electricity demand.

It is preferable that the electricity demand forecasting method further includes a step of disposing a measuring device (a measurement system 2 or a smart meter) for each of the multiple consumers included in the target consumer group. Here, "disposing" means, for example, that the retail electricity supplier installs an electricity meter (smart meter) for each consumer, or that the measurement system 2 is installed in the distribution board 3 of each consumer.

Here, it is preferable that the electricity demand forecasting method forecasts the electricity demand for the next day by taking into account not only the measurement data but also at least one additional piece of information among date and time information, environmental information, and consumer information regarding the consumer.

The electricity demand forecasting method may forecast the electricity demand for the next day by taking into account date and time information. For example, the electricity demand forecasting method may statistically process the past measurement data of each consumer, and when a significant difference in electricity consumption by day of the week is found, may forecast the electricity demand taking into account the day of the week for the next day.

The electricity demand forecasting method may, for example, obtain meteorological information (weather [sunny, cloudy, rainy, snowy, etc.], precipitation, temperature, wind speed, etc.) provided by the Japan Meteorological Agency or a licensed forecasting business operator, and predict the electricity demand for the next day by taking into account the obtained meteorological information (environmental information). For example, the electricity demand forecasting method may statistically process past measurement data of each consumer, and when a significant difference is found between meteorological information such as weather, precipitation, temperature, and wind speed and electricity consumption, predict the electricity demand by taking the meteorological information into account.

Furthermore, the electricity demand forecasting method may, for example, forecast the electricity demand for the next day taking into account customer information (consumer information) held by the retail electricity supplier. The electricity demand forecasting method may, by statistically processing the past measurement data of each consumer, forecast the electricity demand taking into account the customer information (consumer information) when a significant difference in electricity consumption is recognized for the customer information (consumer information).

However, the power demand forecasting method may also forecast the power demand for the next day by taking into account at least two or more additional pieces of information from among date and time information, weather information, and consumer information (customer information). In this way, the power demand forecasting method can improve the accuracy of the forecast by forecasting the power demand by taking into account multiple pieces of additional information.

The power demand forecasting method forecasts the power demand for each consumer in 48 time slots, which are obtained by dividing the 24 hours from midnight of the next day to midnight of the day after that into 30-minute intervals. The power demand forecasting method then generates a power demand plan for each of the 48 time slots for the next day by adding up the forecasted values of the power demand of each consumer for each of the 48 time slots. The power demand forecasting method submits the generated power demand plan to the wholesale power exchange (transmits it to the wholesale power trading management server system 9 via the network NT2). Then, it is preferable that the retail electricity supplier enters into a contract (power purchase and sale contract) for acquiring power with the power transmission and distribution supplier that supplies the power based on the generated power demand plan. It is preferable that the power demand forecasting method performs the power demand forecast in synchronization with the period for generating the power demand plan for the next day, for example, the period between 9:00 p.m. and 11:00 p.m. on the current day.

Incidentally, the power demand forecasting method may forecast power demand using machine learning. The power demand forecasting method uses a trained model to input measurement data and additional information acquired from a measuring instrument (measurement system 2 or a smart meter) and predicts the power demand for the next day. The trained model is generated by machine learning using the measurement data and additional information acquired from each measuring instrument as training data. An example of the machine learning algorithm is XGB (eXtreme Gradient Boosting) regression. However, the machine learning algorithm is not limited to XGB regression, and may be a neural network, a random forest, a decision tree, a logistic regression, a support vector machine (SVM), a Naive Bayes classifier, or k-nearest neighbors. Furthermore, the machine learning algorithm may be a Gaussian Mixture Model (GMM), k-means clustering, or the like. Furthermore, the machine learning learning method used by the prediction device 10 may be any of supervised learning, unsupervised learning, and reinforcement learning.

The power demand forecasting method preferably further includes an authentication process step for authenticating multiple consumers included in the target consumer group. The power demand forecasting method preferably performs a power demand forecast using measurement data acquired from a meter of a consumer who has been successfully authenticated in the authentication process. In other words, the power demand forecasting method can improve the prediction accuracy by using only the measurement data of consumers who have been authorized in advance to forecast power demand.

(5) Summary The resource demand forecasting method according to the first aspect of the present disclosure is used by a business operator supplying resources when generating a resource demand plan that indicates future resource demand in a target consumer group including multiple consumers that use the resources. The resource demand forecasting method according to the first aspect includes a step of measuring the resource consumption by the consumers using measuring devices installed in some or all of the consumers included in the target consumer group, and predicting the future resource demand from the measured data of the consumption in order to generate a resource demand plan.

The resource demand forecasting method according to the first aspect forecasts future resource demand from measurement data obtained by measuring the resource consumption of some or all of the consumers included in the target consumer group using a measuring device, and can therefore assist in the generation of a resource demand plan.

The resource demand forecasting method according to the second aspect of the present disclosure may be realized in combination with the first aspect. In the resource demand forecasting method according to the second aspect, it is preferable to further include a step of placing a measuring instrument for each of a plurality of consumers included in the target consumer group.

The resource demand forecasting method according to the second aspect can easily obtain measurement data for multiple consumers by placing a measuring instrument for each of the multiple consumers included in the target consumer group.

The resource demand forecasting method according to the third aspect of the present disclosure can be realized in combination with the first or second aspect. The resource demand forecasting method according to the third aspect further includes a step of generating a resource demand plan using the resource demand forecast result.

The resource demand forecasting method according to the third aspect can automatically generate a resource demand plan.

The resource demand forecasting method according to the fourth aspect of the present disclosure can be realized in combination with any of the first to third aspects. In the resource demand forecasting method according to the fourth aspect, it is preferable that the resource includes electricity supplied by a retail electricity supplier.

The resource demand forecasting method according to the fourth aspect can assist in generating electricity demand plans.

The resource demand forecasting method according to the fifth aspect of the present disclosure can be realized by combining with any of the first to fourth aspects. In the resource demand forecasting method according to the fifth aspect, it is preferable that the business operator enters into a contract for acquiring resources with a higher-level business operator that supplies resources to the business operator based on a resource demand plan.

The resource demand forecasting method according to the fifth aspect makes it possible to acquire resources at an appropriate price by entering into a contract for acquiring resources based on a resource demand plan.

The resource demand forecasting method according to the sixth aspect of the present disclosure can be realized by combining with any of the first to fifth aspects. In the resource demand forecasting method according to the sixth aspect, it is preferable that the meter includes a main metering unit that measures the consumption of resources at a single location in the consumer.

The resource demand forecasting method according to the sixth aspect can more accurately measure the total consumption of resources at a single consumer by including a main measuring unit, such as an electricity meter (smart meter), gas meter, or water meter, in the measuring instrument.

The resource demand prediction method according to the seventh aspect of the present disclosure may be realized by combining with any one of the first to sixth aspects. It is preferable that the resource demand prediction method according to the seventh aspect further includes a step of acquiring, as additional information other than the resource consumption amount, at least one of consumer information about the consumer and environmental information about the environment in which the consumer is located. It is preferable that the resource demand prediction method according to the seventh aspect predicts resource demand taking into account the additional information.

The resource demand forecasting method according to the seventh aspect can improve the accuracy of forecasting resource demand by forecasting resource demand while taking into account additional information.

The resource demand forecasting method according to the eighth aspect of the present disclosure can be realized in combination with any of the first to seventh aspects. In the resource demand forecasting method according to the eighth aspect, it is preferable to forecast resource demand in synchronization with the period for generating the resource demand plan.

The resource demand forecasting method according to the eighth aspect can forecast resource demand at an appropriate time to assist in generating a resource demand plan.

The resource demand forecasting method according to the ninth aspect of the present disclosure can be realized in combination with any of the first to eighth aspects. It is preferable that the resource demand forecasting method according to the ninth aspect forecasts resource demand using a trained model.

The resource demand forecasting method according to the ninth aspect can improve the accuracy of forecasting resource demand by forecasting resource demand using a trained model.

The resource demand forecasting method according to the tenth aspect of the present disclosure may be realized in combination with any one of the first to ninth aspects. It is preferable that the resource demand forecasting method according to the tenth aspect further includes an authentication process step for authenticating a plurality of consumers included in the target consumer group. It is preferable that the resource demand forecasting method according to the tenth aspect forecasts resource demand using measurement data acquired from a meter of a consumer that has been successfully authenticated in the authentication process.

The resource demand forecasting method according to the tenth aspect can improve forecast accuracy by using only measurement data from pre-authorized consumers to forecast power demand.

The program according to the eleventh aspect of the present disclosure causes one or more processors (prediction device 10) to execute the resource demand prediction method according to any one of the first to tenth aspects.

The program according to the eleventh aspect can automatically predict resource demand.

A resource demand forecasting system (electricity demand forecasting system X1) according to a twelfth aspect of the present disclosure includes a plurality of measuring instruments (20) disposed at each of a plurality of consumers to which resources are supplied by a business operator, and a server (server system 1) that collects measurement data measured by the measuring instruments (20) on the consumption of resources consumed by the plurality of consumers. The server predicts future resource demand from the measurement data of consumption obtained from the plurality of measuring instruments (20) in order to generate a resource demand plan.

The resource demand forecasting system according to the twelfth aspect forecasts future resource demand from measurement data obtained by measuring the resource consumption of some or all of the consumers included in the target consumer group using a measuring device (20), and can therefore assist in the generation of a resource demand plan.

A distribution board (3) according to a thirteenth aspect of the present disclosure is used in the resource demand forecasting system according to the twelfth aspect. The distribution board (3) according to the thirteenth aspect has a main breaker (30), one or more branch breakers (31) that are branch-connected to the load side terminal of the main breaker (30), and a meter (20) that measures the power consumed by the load (4) via the main breaker (30) and the branch breaker (31).

The distribution board (3) according to the thirteenth aspect of the present disclosure has a meter (20) that measures the power consumed by the load (4), so that the meter (20) can be easily positioned and can assist in generating a resource demand plan.

X1 Electricity demand forecasting system (resource demand forecasting system)
1. Server system (server)
3 Distribution board 4 Load 10 Prediction device (processor)
20 Measuring instrument 30 Main breaker 31 Branch breaker

Claims (12)

1. A resource demand forecasting method used by a business operator supplying resources when generating a resource demand plan representing future resource demand in a target consumer group including a plurality of consumers using the resources, comprising:
a step of measuring the consumption of the resources by the consumers using measuring instruments installed in some or all of the consumers included in the target consumer group, and predicting the resource demand for the next day in synchronization with the night of the day, which is a period for generating the resource demand plan for the next day , in order to generate the resource demand plan for the next day from the measurement data of the consumption amount for the day;
Resource demand forecasting methods. The method further includes a step of disposing the measuring device for each of the plurality of consumers included in the target consumer group.
The resource demand forecasting method according to claim 1 . generating the resource demand plan using the resource demand forecast result;
The resource demand forecasting method according to claim 1 or 2. The resources include electricity supplied from a retail electricity supplier corresponding to the utility.
The resource demand forecasting method according to any one of claims 1 to 3. the business operator makes a contract for acquiring the resources with a higher-level business operator that supplies the resources to the business operator based on the resource demand plan;
The resource demand forecasting method according to any one of claims 1 to 4. The meter includes a main metering unit that measures the consumption of the resource at one location of the consumer,
The resource demand forecasting method according to any one of claims 1 to 5. The method further includes a step of acquiring at least one of consumer information about the consumer and environmental information about an environment in which the consumer is located as additional information other than the amount of consumption of the resource,
predicting the resource demand taking into account the additional information;
The resource demand forecasting method according to any one of claims 1 to 6. forecasting the resource demand using the trained model;
The resource demand forecasting method according to any one of claims 1 to 7. The method further includes a step of performing an authentication process to authenticate the plurality of consumers included in the target consumer group,
predicting the resource demand using the measurement data acquired from the meter of the consumer that has been successfully authenticated in the authentication process;
The resource demand forecasting method according to any one of claims 1 to 8. A program for causing one or more processors to execute the resource demand forecasting method described in any one of claims 1 to 9. A plurality of measuring devices installed in each of a plurality of consumers to which resources are supplied from a business operator;
a server that collects measurement data obtained by measuring the consumption of the resources consumed by the plurality of consumers using the measuring device;
Equipped with
the server predicts resource demand for the next day in synchronization with the night of the day, which is a period for generating the resource demand plan for the next day , in order to generate a resource demand plan for the next day from the measurement data of the consumption amount for the day acquired from the plurality of measuring devices;
Resource demand forecasting system. The resource demand forecasting system according to claim 11 is used,
The main breaker and
One or more branch breakers are branch-connected to a load side terminal of the main breaker;
The measuring instrument;
having
The meter measures the power consumed by a load via the main breaker and the branch breaker as the resource.
Distribution board.

Images