Electricity demand forecasting system and electricity demand forecasting method

Abstract

To provide a system which is effective for improvement of prediction accuracy of power demand.SOLUTION: A power demand prediction system 10 comprises: a data accumulation unit 114 which accumulates teacher data including result data of a fluctuation factor of power demand and result data of the power demand for each of a plurality of customer groups of a power selling company in a database; a model generation unit 116 which generates a demand prediction model 118 representing a relation between fluctuation factor data and power demand data on the basis of a plurality of pieces of teacher data accumulated in the database for each of the plurality of customer groups; an input information acquisition unit 112 which acquires prediction data of the fluctuation factor for each of the plurality of customer groups; an individual demand prediction unit 121 which calculates prediction data of the power demand on the basis of the prediction data of the fluctuation factor and the demand prediction model 118 for each of the plurality of customer groups; and a total demand prediction unit 122 which calculates prediction data of the total power demand to the power selling company on the basis of the prediction data of the power demand calculated for each of the plurality of customer groups.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present disclosure relates to an electricity demand forecasting system and an electricity demand forecasting method. [Background technology]

[0002] Patent Document 1 discloses an electricity demand forecasting system that includes a model creation unit that generates a model for predicting electricity demand by performing machine learning using learning data in which actual electricity data, which is the actual electricity demand, is associated with actual variable factor data, which is the actual results of variable factors that may cause fluctuations in electricity demand, and a forecasting unit that uses the model to forecast electricity demand on a target forecast date. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2019-204459 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present disclosure provides a system that is effective in improving the accuracy of electricity demand forecasts. [Means for solving the problem]

[0005] An electricity demand forecasting system according to one aspect of the present disclosure includes a data storage unit that accumulates in a database, for each of a plurality of customer groups of an electricity seller, training data including actual data on electricity demand fluctuation factors and actual electricity demand data; a model generation unit that generates, for each of the plurality of customer groups, a demand forecast model representing the relationship between the fluctuation factor data and electricity demand data based on the plurality of training data accumulated in the database; an input information acquisition unit that acquires predicted data of the fluctuation factors for each of the plurality of customer groups; an individual demand forecasting unit that calculates predicted data of electricity demand based on the predicted data of the fluctuation factors and the demand forecast model for each of the plurality of customer groups; and a total demand forecasting unit that calculates predicted data of total electricity demand for the electricity seller based on the predicted data of electricity demand calculated for each of the plurality of customer groups, wherein the model generation unit generates, for one customer group, an electricity demand forecasting model representing the relationship between the fluctuation factor data including one fluctuation factor and the electricity demand data, and generates, for other customer groups, an electricity demand forecasting model representing the relationship between the fluctuation factor data including other fluctuation factors not included in the fluctuation factor data for the one customer group and the electricity demand data.

[0006] An electricity demand forecasting system according to another aspect of the present disclosure includes a data storage unit that accumulates in a database, for each of a plurality of customer groups of an electricity seller, training data including actual data on electricity demand fluctuation factors and actual electricity demand data; a model generation unit that generates, for each of the plurality of customer groups, a demand forecast model representing the relationship between the fluctuation factor data and electricity demand data based on the plurality of training data accumulated in the database; an input information acquisition unit that acquires predicted data of the fluctuation factors for each of the plurality of customer groups; an individual demand forecasting unit that calculates predicted data of electricity demand for each of the plurality of customer groups based on the predicted data of the fluctuation factors and the demand forecasting model; and a total demand forecasting unit that calculates predicted data of total electricity demand for the electricity seller based on the predicted data of the electricity demand calculated for each of the plurality of customer groups, wherein the plurality of customer groups are grouped according to differences in the fluctuation factors that affect the electricity demand, and customers who are likely to have a similar relationship between the fluctuation factor data and the electricity demand data belong to the same customer group.

[0007] A power demand forecasting method according to still another aspect of the present disclosure includes: accumulating, for each of a plurality of customer groups of a power seller, training data including actual data of power demand fluctuation factors and actual data of power demand in a database; generating, for each of the plurality of customer groups, a demand forecasting model expressing a relationship between the fluctuation factor data and power demand data based on the plurality of training data accumulated in the database; acquiring prediction data of the fluctuation factors for each of the plurality of customer groups; calculating prediction data of power demand based on the prediction data of the fluctuation factors and the demand forecasting model for each of the plurality of customer groups; and calculating prediction data of total power demand for the power seller based on the prediction data of the power demand calculated for each of the plurality of customer groups. Generating the demand forecasting model includes generating, for one customer group, a power demand forecasting model expressing a relationship between the fluctuation factor data including one fluctuation factor and the power demand data, and generating, for another customer group, a power demand forecasting model expressing a relationship between the fluctuation factor data including another fluctuation factor not included in the fluctuation factor data for the one customer group and the power demand data.

[0008] An electricity demand forecasting method according to yet another aspect of the present disclosure includes: accumulating in a database, for each of a plurality of customer groups of an electricity seller, training data including actual data on electricity demand fluctuation factors and actual data on electricity demand; generating, for each of the plurality of customer groups, a demand forecast model representing a relationship between the fluctuation factor data and electricity demand data based on the plurality of training data accumulated in the database; obtaining forecast data of the fluctuation factors for each of the plurality of customer groups; calculating forecast data of electricity demand for each of the plurality of customer groups based on the forecast data of the fluctuation factors and the demand forecast model; and calculating forecast data of total electricity demand for the electricity seller based on the forecast data of electricity demand calculated for each of the plurality of customer groups, wherein the plurality of customer groups are grouped according to differences in the fluctuation factors affecting the electricity demand, and customers who are likely to have a similar relationship between the fluctuation factor data and the electricity demand data belong to the same customer group. Effect of the Invention

[0009] According to the present disclosure, it is possible to provide a system and method that are effective in improving the accuracy of power demand forecasts. [Brief description of the drawings]

[0010] [Figure 1] 1 is a schematic diagram illustrating a configuration of a power supply and demand adjustment system. [Diagram 2] FIG. 1 is a block diagram illustrating a configuration of a power demand forecasting system. [Diagram 3] 13 is a table illustrating an example of a forecast result of individual demand. [Figure 4] FIG. 13 is a block diagram showing a modified example of the power demand prediction system. [Diagram 5] FIG. 13 is a schematic diagram showing an example of a first evaluation screen. [Figure 6] FIG. 13 is a schematic diagram showing an example of a second evaluation screen. [Figure 7] 1 is a flowchart illustrating a procedure for generating a demand forecasting model. [Figure 8] 1 is a flowchart illustrating a demand forecasting procedure. [Figure 9] 11 is a flowchart illustrating a procedure for correcting a demand forecasting model. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] Hereinafter, the embodiments will be described in detail with reference to the drawings. In the description, the same elements or elements having the same functions are denoted by the same reference numerals, and duplicated description will be omitted.

[0012] [Electricity supply and demand adjustment system] (Overall composition) The electricity supply and demand adjustment system 1 shown in FIG. 1 is a system that adjusts the relationship between the demand and supply of electricity at a retail electricity supplier. The retail electricity business is a business that supplies electricity generated by a company's own power generation facilities, electricity purchased from a wholesale electricity exchange, etc., to customers (consumers). The retail electricity supplier supplies electricity to customers via the power grid of a power company. Specific examples of customers include customers who consume electricity mainly in offices and customers who consume electricity mainly in production factories. There are no particular restrictions on the facilities where a customer consumes electricity, and so the customer is not limited to an office or a production factory. For example, the facility where a customer mainly consumes electricity may be a waste treatment facility or a store.

[0013] To ensure the stability of the power grid, electricity retailers are required to "balance the supply and demand at the same time." "Balancing the supply and demand at the same time" refers to suppressing the discrepancy between the demand and supply of electricity in 30-minute increments. Under the "balancing the supply and demand at the same time" system, electricity retailers are required to submit a next-day supply and demand plan in 30-minute increments in advance to the Organization for Cross-regional Coordination of Transmission Operators of Japan. Electricity retailers subsequently settle an amount proportional to the difference (imbalance) between the submitted next-day supply and demand plan and the actual supply and demand. Electricity supply and demand adjustment system 1 is a system that supports electricity retailers under the "balancing the supply and demand at the same time" system.

[0014] 1, the power supply and demand adjustment system 1 includes a power demand forecasting system 10, a data collection system 20, and an administrator terminal 30. The power demand forecasting system 10, the data collection system 20, and the administrator terminal 30 are connected to each other via a communication network NW such as the Internet.

[0015] The power demand forecasting system 10 is a system that forecasts power demand for retail electricity businesses (power sellers). For example, the power demand forecasting system 10 forecasts the next day's power demand for retail electricity businesses in 30-minute increments. Note that "30-minute increments" is merely an example, and there is no particular limit to the time unit in which the power demand is forecast. For example, the power demand forecasting system 10 may forecast the power demand in 1-hour increments, 15-minute increments, etc.

[0016] The data collection system 20 is connected to watt-hour meters 41 of multiple customers 40 of the electricity retailer via a communication network NW, and acquires actual data on electricity demand (e.g., actual data on consumed electricity) from each watt-hour meter 41. The data collection system 20 may acquire the actual data on electricity demand from a server of a power company that owns the power system.

[0017] The data collection system 20 also acquires performance data on fluctuation factors for multiple customers 40. The fluctuation factors include any factor that can cause fluctuations in electricity demand. Specific examples of fluctuation factors include weather, day of the week, and whether it is a business day or a holiday. If the customer 40 is a production factory, the production status can also be a fluctuation factor. The production status includes the type and number of products produced in a specified period of time. As an example, the data collection system 20 acquires the production status from the consumer by regular transmission.

[0018] Furthermore, the data collection system 20 also acquires actual data on the amount of power generated in the electricity retailer's own power generation facility. For example, the data collection system 20 is connected to a watt-hour meter 51 of the electricity retailer's power generation facility 50 via a communication network NW, and acquires actual data on the amount of power generated from the watt-hour meter 51. The data collection system 20 may acquire actual data on power demand from a server of a power company that owns the power system.

[0019] The manager terminal 30 is a terminal used by the manager of the electricity retailer, and has a monitor (display) and an input device. Specific examples of the monitor include a liquid crystal monitor and an organic electroluminescence monitor. Specific examples of the input device include a keyboard and a mouse. The input device may be integrated with the monitor as a so-called touch panel.

[0020] (Electricity demand forecasting system) The configuration of the power demand forecasting system 10 will be described in detail below. The power demand forecasting system 10 is configured to execute the following operations: for each of a plurality of customer groups of a retail electricity supplier, accumulate in a database training data including actual data on fluctuation factors of power demand and actual data on power demand, generate, for each of the plurality of customer groups, a demand forecasting model representing a relationship between the fluctuation factor data and power demand data based on the plurality of training data accumulated in the database, acquire prediction data of the fluctuation factors for each of the plurality of customer groups, calculate prediction data of power demand for each of the plurality of customer groups based on the prediction data of the fluctuation factors and the demand forecasting model, and calculate prediction data of total power demand for the power seller based on the prediction data of power demand calculated for each of the plurality of customer groups.

[0021] The variable factors may vary greatly depending on the customer. For example, for customers who consume electricity in offices, weather and the like tend to be major factors, whereas for customers who consume electricity in production factories, production conditions tend to be major factors. In response to this, the power demand forecasting system generates a power demand forecasting model for each of a plurality of customer groups, calculates forecast data of power demand for each of the plurality of customer groups based on the power demand forecasting model, and calculates forecast data of total power demand for the power seller based on the forecast data of power demand calculated for each of the plurality of customer groups. For this reason, it is possible to group customers according to differences in factors affecting power demand, forecast the power demand for each group with high accuracy, and aggregate the forecast results to forecast the total power demand with high accuracy. This is therefore effective in improving the accuracy of forecasting power demand.

[0022] As illustrated in FIG. 1, the power demand forecasting system 10 includes a circuit 190. The circuit 190 includes a processor 191, a memory 192, a storage 193, and a communication port 194. The storage 193 includes one or more non-volatile storage media (e.g., a hard disk or a flash memory) that can be read by a computer. The storage 193 stores a program for causing the power demand forecasting system 10 to execute the following operations: storing, for each of a plurality of customer groups of a retail electricity supplier, training data including actual data of fluctuation factors of power demand and actual data of power demand in a database; generating, for each of the plurality of customer groups, a demand forecasting model that represents a relationship between the fluctuation factor data and power demand data based on the plurality of training data stored in the database; acquiring prediction data of the fluctuation factors for each of the plurality of customer groups; calculating, for each of the plurality of customer groups, prediction data of power demand based on the prediction data of the fluctuation factors and the demand forecasting model; and calculating prediction data of a total power demand for the power seller based on the prediction data of the power demand calculated for each of the plurality of customer groups. The storage 193 may include an area for storing the above-mentioned programs, as well as an area for storing the above-mentioned database.

[0023] The memory 192 includes one or more volatile storage media (e.g., random access memory). The memory 192 temporarily stores the programs loaded from the storage 193 and the results of calculations by the processor 191. The processor 191 includes one or more calculation devices. The processor 191 executes the above programs in cooperation with the memory 192. The communication port 194 is connected to the data collection system 20 and the administrator terminal 30 via the communication network NW, and performs information communication between the data collection system 20 and the administrator terminal 30 in response to a command from the processor 191. Note that the connection here means that information communication is possible, and does not necessarily mean a physical connection by wire. The same applies hereinafter. In other words, at least a part of the communication network NW may be a wireless communication path.

[0024] FIG. 1 illustrates an example in which the power demand forecasting system 10 is configured by one set of computers, but the power demand forecasting system 10 may also be configured by multiple sets of computers connected to each other via a communication network NW.

[0025] 2 is a block diagram showing various functions realized by the processor 191 executing the above program in cooperation with the memory 192, as block-shaped components (hereinafter referred to as "functional blocks"). In the example of FIG. 2, the power demand prediction system 10 has, as functional blocks, a performance information acquisition unit 111, an encoding processing unit 113, a data accumulation unit 114, a learning database 115, a model generation unit 116, a model holding unit 117, an input information acquisition unit 112, an individual demand prediction unit 121, a total demand prediction unit 122, and a prediction result output unit 123.

[0026] The performance information acquisition unit 111 acquires performance data of fluctuation factors of power demand and corresponding performance data of power demand from the data collection system 20 for each of the multiple customer groups. The performance data of power demand corresponds to the performance data of the fluctuation factors, meaning that the target period of the performance data of power demand and the target period of the performance data of the fluctuation factors at least partially overlap. The multiple customer groups are groups that classify multiple customers of the retail electricity supplier. The multiple customers are grouped such that customers who are likely to have a similar relationship between the fluctuation factor data and the power demand data belong to the same customer group. Each of the multiple customer groups includes one or more customers of the retail electricity supplier. The performance information acquisition unit 111 acquires performance data indicating the actual value of the power consumption amount for each of 48 time frames obtained by dividing 24 hours into 30 minutes for each of the multiple customer groups.

[0027] The performance information acquiring unit 111 may acquire performance data of different variable factors for two or more customer groups among the multiple customer groups. For example, the multiple customer groups may include a first group that consumes electricity in an office and a second group that consumes electricity in a production factory.

[0028] In the first group, there is a tendency for the relationship between the power demand data and the variable factor data including at least the weather to be strong. Therefore, the performance information acquisition unit 111 may acquire performance data of the variable factors including at least the weather for the first group. As an example, the performance information acquisition unit 111 acquires performance data of the variable factors including the weather, the day of the week, and whether it is a business day or a holiday for the first group.

[0029] In the second group, there is a tendency for the relationship between the variable factor data including at least the production status and the power demand data to be strong. Therefore, the performance information acquisition unit 111 may acquire performance data of the variable factors including at least the production status for the second group. As an example, the performance information acquisition unit 111 acquires performance data of the variable factors including the weather and the production status for the second group. The production status includes, for example, information representing the production object and the production quantity. In the second group, there may be a weak relationship between the weather and the power demand data. In this case, the variable factors of the second group do not need to include the weather.

[0030] The relationship between the fluctuation factor data and the power demand data tends to be more similar between customers who consume power in offices than between customers who consume power in manufacturing plants. Therefore, the number of customers included in the first group may be greater than the number of customers included in the second group. The number of customers included in the second group may be 1.

[0031] The relationship between the fluctuation factor data and the electricity demand data tends to be significantly different between customers who consume electricity in a production factory due to differences in the production objects. Therefore, the multiple customer groups may further include a third group that consumes electricity in a production factory where the production objects are different from those of the production factory where the second group consumes electricity. The number of customers included in the third group may be one. The production objects being different from each other includes the production objects being the same type but having different specifications from each other, and the production objects being different in type from each other. As an example, the performance information acquisition unit 111 also acquires performance data of the fluctuation factors including the weather and the production status for the third group. The production status includes, for example, information representing the production object and the production quantity. In the third group, the relationship between the weather and the electricity demand data may be weak. In this case, the fluctuation factors of the third group do not need to include the weather.

[0032] When the performance data of the variable factors includes a categorical variable, the encoding processing unit 113 converts the categorical variable into a quantitative variable by encoding. For example, the performance data of the production status may include a categorical variable representing the production status. A specific example of a categorical variable representing the production status is the name of a production pattern in which the production quantity for each type of production object is determined. In this case, the encoding processing unit 113 quantifies the production status by encoding. For example, the encoding processing unit 113 converts the categorical variable representing the production status into a quantitative variable by encoding.

[0033] The encoding process at this time may include a target encoding process in which the magnitude of the power demand is used as a target variable. For example, the encoding processing unit 113 converts a categorical variable representing the production status into a quantitative variable representing the magnitude of the power demand based on a conversion table prepared in advance. The conversion table, for example, determines a numerical value representing the magnitude of the power demand for each of a plurality of categorical variables. Hereinafter, the numerical value representing the magnitude of the power demand is referred to as a "power demand value." For example, the encoding processing unit 113 extracts the power demand value associated with the categorical variable representing the production status from the conversion table as the value of the quantitative variable.

[0034] The encoding process executed by the encoding unit 113 is not necessarily limited to the target encoding process. The encoding unit 113 may convert categorical variables into quantitative variables by label encoding, or may convert categorical variables into quantitative variables by one-hot encoding.

[0035] The data accumulation unit 114 accumulates teacher data including actual data on fluctuation factors of power demand and actual data on power demand in a learning database 115 (database) for each of a plurality of customer groups. For example, the data accumulation unit 114 accumulates actual data on fluctuation factors of power demand acquired by the actual information acquisition unit 111 from the data collection system 20 and actual data on the corresponding power demand in the learning database 115 for each of a plurality of customer groups. When the actual data on fluctuation factors of power demand includes a categorical variable, the data accumulation unit 114 may accumulate the actual data on the fluctuation factors after the categorical variable is converted into a quantitative variable by the encoding processing unit 113 in the learning database 115.

[0036] The model generation unit 116 generates, for each of a plurality of customer groups, a demand forecast model that represents the relationship between the fluctuation factor data and the electricity demand data, based on a plurality of teacher data stored in the learning database 115.

[0037] The model generation unit 116 may generate a demand forecasting model by at least the random forest method. In the random forest method, the model generation unit 116 randomly generates a plurality of decision trees based on the performance data of the variable factors included in the teacher data, and constructs each of the plurality of decision trees based on the teacher data. For example, the model generation unit 116 constructs the decision tree to be constructed as follows. That is, the model generation unit 116 randomly samples the teacher data, provides the sampled data to the top node of the decision tree, and obtains a branch function for the node. Next, the model generation unit 116 divides the teacher data into two systems based on the branch function, provides the two nodes with the node as a parent, and obtains the branch function for the two nodes. Thereafter, the model generation unit 116 continues the above process for the lower nodes until a preset end condition is satisfied, and completes the construction of the decision tree.

[0038] The model generation unit 116 may generate a demand forecasting model by ensemble learning that combines a random forest method with one or more other learning methods. The one or more other learning methods may include at least a recurrent neural network method. A recurrent neural network is a neural network having a recurrent structure that loops information stored in the past in an intermediate layer. The recurrent structure gives the neural network memory, improving the prediction accuracy for time series data.

[0039] The model generation unit 116 may generate a first demand prediction model for the first group that represents the relationship between the variable factor data including at least the weather and the electricity demand data, generate a second demand prediction model for the second group that represents the relationship between the variable factor data including at least the production status and the electricity demand data, and generate a third demand prediction model for the third group that represents the relationship between the variable factor data including at least the production status and the electricity demand data.

[0040] As an example, the model generation unit 116 generates a first demand prediction model that represents the relationship between the power demand data and the fluctuation factor data, including the weather, the day of the week, and whether it is a business day or a holiday. The model generation unit 116 generates a second demand prediction model and a third demand prediction model that represent the relationship between the power demand data and the fluctuation factor data, including the weather and the production status.

[0041] As described above, the data accumulation unit 114 may include in the performance data of the variation factors the results of the categorical variables converted into quantitative variables by the encoding processing unit 113. In this case, the variation factor data including the production status may include data obtained by digitizing the production status by encoding processing.

[0042] The model generation unit 116 stores the demand forecast model 118 generated for each of the multiple customer groups in the model storage unit 117.

[0043] The input information acquisition unit 112 acquires prediction data of fluctuation factors of power demand for each of a plurality of customer groups. For example, the input information acquisition unit 112 acquires prediction data of fluctuation factors of power demand for each of a plurality of customer groups from the data collection system 20. For example, the input information acquisition unit 112 acquires prediction data of the fluctuation factors for the next day from the data collection system 20.

[0044] The attributes of the prediction data of the variation factors acquired by the input information acquisition unit 112 for each of the multiple customer groups are equal to the attributes of the actual data of the variation factors acquired by the actual data acquisition unit 111 for each of the multiple customer groups. For example, the input information acquisition unit 112 acquires prediction data of the variation factors including the weather, the day of the week, and whether it is a business day or a holiday for the first group, and acquires prediction data of the variation factors including the weather and the production status for the second and third groups. The prediction data of the production status corresponds to a production plan or a factory operation plan. Even if the prediction data of the variation factors includes categorical variables, the encoding processing unit 113 converts the categorical variables into quantitative variables by encoding processing.

[0045] The individual demand forecasting unit 121 calculates, for each of a plurality of customer groups, forecast data of power demand based on the forecast data of the fluctuation factors acquired by the input information acquiring unit 112 and the demand forecast model 118 stored in the model holding unit 117. When the forecast data of the fluctuation factors of power demand includes a categorical variable, the individual demand forecasting unit 121 may calculate the forecast data of power demand based on the forecast data of the fluctuation factors after the categorical variable is converted into a quantitative variable by the encoding processing unit 113 and the demand forecast model 118 stored in the model holding unit 117.

[0046] The format of the power demand forecast data generated by the individual demand forecasting unit 121 for each of the multiple customer groups may be the same as the format of the power demand historical data acquired by the historical information acquiring unit 111 for each of the multiple customer groups. For example, the individual demand forecasting unit 121 calculates, for each of the multiple customer groups, forecast data indicating a predicted value of the amount of power consumption for each of 48 time frames obtained by dividing 24 hours into 30-minute frames, as illustrated in Fig. 3. In the figure, each horizontal line represents the power demand forecast data for one customer group.

[0047] The total demand forecasting unit 122 calculates forecast data of total power demand for the electricity retailer based on the forecast data of power demand calculated for each of the multiple customer groups. For example, the total demand forecasting unit 122 calculates forecast data of total power demand indicating the total power consumption for each of the 48 time frames by adding up the forecast values ​​of the power consumption for each of the multiple customer groups for each of the above-mentioned 48 time frames.

[0048] The prediction result output unit 123 outputs the prediction data of the total power demand calculated by the total demand prediction unit 122. Outputting the prediction data includes displaying the prediction data. For example, the prediction result output unit 123 displays the prediction data on the monitor of the manager terminal 30.

[0049] The power demand forecasting system 10 may be further configured to evaluate an error between the power demand forecast data and the power demand actual data for each of the multiple customer groups. In this case, the power demand forecasting system 10 may be further configured to correct new power demand forecast data for each of the multiple customer groups based on the error evaluation result.

[0050] The power demand forecasting system 10 may be configured to further correct the demand forecasting model for each of the multiple customer groups based on the error evaluation result. For example, as shown in Fig. 4, the power demand forecasting system 10 further includes, as functional blocks, an error evaluation unit 131, an evaluation image display unit 132, a prediction correction unit 133, and a model correction unit 134.

[0051] The error evaluation unit 131 evaluates the error between the forecast data of the power demand and the actual data of the power demand for each of a plurality of customer groups. For example, the error evaluation unit 131 calculates the difference between the forecast value and the actual value for each unit time (for example, 30 minutes) and sums up the calculation results of the difference for one day. There is no particular limit to the period to be summed, and it may be shorter or longer than one day. For example, the error evaluation unit 131 calculates the difference between the forecast value and the actual value of the power consumption for each of the above 48 frames of time, sums up the calculation results of the difference for each of the 48 frames of time, and calculates the sum as the evaluation value of the error. According to this calculation method, the positive difference and the negative difference cancel each other out. Therefore, when the positive and negative of the difference randomly changes for each time frame, the absolute value of the evaluation value becomes small. On the other hand, when the positive and negative of the difference tend to match between the time frames, the absolute value of the evaluation value tends to become large. When the prediction accuracy of the demand forecast model itself decreases, it is considered that the positive and negative of the difference will easily match between the time frames. Therefore, according to the above calculation method, it is possible to calculate an evaluation value that is likely to represent a decrease in the prediction accuracy of the demand prediction model itself.

[0052] The evaluation image display unit 132 displays an evaluation image that visualizes the evaluation result of the error for each of the multiple customer groups on a monitor. For example, the evaluation image display unit 132 displays the evaluation image on the monitor of the manager terminal 30. For example, the evaluation image display unit 132 may display a first evaluation image that visualizes the change over time of the evaluation result of the error for each of the multiple customer groups on a monitor.

[0053] As an example, the evaluation image display unit 132 may display on the monitor a first evaluation image in which colors representing the evaluation results of the error are mapped according to a plurality of customer groups and the time of evaluation. For example, as shown in FIG. 5, the evaluation image display unit 132 displays on the monitor a first evaluation image 140 in which cells 141 indicating the above-mentioned evaluation value by at least one of the three color attributes (hue, brightness, saturation) are mapped according to a plurality of customer groups and the time of evaluation. Each cell 141 may represent the evaluation result of the error by hatching or dots, etc. Note that the average color of the cell 141 may change depending on the pattern shape, thickness, size, or density of the hatching or dots. Therefore, representing the evaluation result of the error by hatching or dots, etc., may be an example of representing the evaluation result of the error by color.

[0054] In the example of Fig. 5, a horizontal row shows the evaluation value for one customer group on a daily basis. The multiple cells 141 in this row are arranged so that the evaluation date becomes more recent as you move to the right. This arrangement makes it possible to visualize the change in evaluation value from day to day (an example of change over time). A vertical row shows the evaluation value for each of multiple customer groups on a day.

[0055] 5, the area is divided into a latest data display area 143 that contains evaluation values ​​for the week including the latest date, and a past data display area 142 that contains evaluation values ​​prior to that. In the latest date column, no cell 141 is placed in a location where an evaluation value has not yet been calculated.

[0056] The evaluation image display unit 132 may display on the monitor a second evaluation image that visualizes the relationship between the evaluation result of the error and the correction criterion of the demand forecasting model for each of the multiple customer groups. The evaluation image display unit 132 may display on the monitor an evaluation image including a bar graph showing the evaluation result of the error and a correction criterion line showing the correction criterion of the demand forecasting model for each of the multiple customer groups.

[0057] For example, as shown in FIG. 6, the evaluation image display unit 132 may display on the monitor a second evaluation image 150 including a bar graph 151 representing the absolute value of the above-mentioned evaluation value and a correction reference line 152 representing the correction reference of the demand forecasting model for each of a plurality of customer groups. According to FIG. 6, depending on whether the bar graph 151 reaches the correction reference line 152 or not, the necessity of correction of the demand forecasting model is clearly indicated for each of a plurality of customer groups. As an example, the evaluation image display unit 132 displays both the first evaluation image 140 and the second evaluation image 150 on the monitor of the administrator terminal 30. The evaluation image display unit 132 may change the color of each bar graph 151 based on the relationship between the correction reference and the evaluation result of the error. For example, a bar graph that exceeds the correction reference (correction reference line 152) may be displayed in a first color (e.g., red), a bar graph that is likely to exceed the correction reference (within a predetermined range from the correction reference) may be displayed in a second color (e.g., orange), and a bar graph that falls below the correction reference by a predetermined value or more may be displayed in a third color (e.g., blue). Also, as shown in Fig. 6, the color of each bar graph may be changed for each part based on its relationship with the correction criterion. For example, the part of the bar graph that has exceeded the correction criterion may be shown in a first color, the part that is likely to exceed the correction criterion may be shown in a second color, and the part that is below the correction criterion by a predetermined level or more may be shown in a third color. A gradation may be applied between the colors.

[0058] The prediction correction unit 133 corrects new predicted data of power demand for each of the multiple customer groups based on the error evaluation result. The prediction correction unit 133 may correct the new predicted data of power demand for each of the multiple customer groups based on a user input after the first evaluation image 140 is displayed. For example, the prediction correction unit 133 may correct the new predicted data of power demand based on an input from an input device of the manager terminal 30 after causing the first evaluation image 140 to be displayed on the monitor of the manager terminal 30. The prediction correction unit 133 may automatically correct the new predicted data of power demand based on the error evaluation result. A specific example of the automatic correction may be adding or subtracting the error evaluation result to or from the new predicted data of power demand.

[0059] The first evaluation image 140 indicates whether the tendency of the evaluation value to be positive or negative continues over multiple days. If the tendency of the evaluation value to be positive or negative continues over multiple days, the same tendency is likely to appear in the new predicted data. Therefore, by correcting the new predicted data so as to cancel the tendency of the evaluation value to be positive or negative, it is possible to improve the accuracy of the predicted data.

[0060] The model correction unit 134 corrects the demand forecast model for each of the multiple customer groups based on the error evaluation result. The model correction unit 134 may correct the demand forecast model for each of the multiple customer groups based on a user input after displaying the second evaluation image 150. For example, the model correction unit 134 may correct the demand forecast model based on an input via an input device of the administrator terminal 30 after displaying the second evaluation image 150 on the monitor of the administrator terminal 30.

[0061] As an example, when an instruction to correct the demand forecast model is input by an input device of the administrator terminal 30, the model correction unit 134 requests the model generation unit 116 to regenerate the demand forecast model. When regeneration of the demand forecast model is requested by the model correction unit 134, the model generation unit 116 regenerates the demand forecast model based on the teacher data accumulated in the learning database 115 at that time, and stores the regenerated demand forecast model in the model holding unit 117. The model correction unit 134 may automatically correct the demand forecast model based on the error evaluation result. For example, the model correction unit 134 may automatically request the model generation unit 116 to regenerate the demand forecast model based on the error evaluation result.

[0062] [Electricity demand forecast procedure] As an example of an electricity demand forecasting method, an electricity demand forecasting procedure executed by the electricity demand forecasting system 10 is illustrated. This procedure includes accumulating, for each of a plurality of customer groups of the retail electricity supplier, training data including actual data on fluctuation factors of electricity demand and actual data on electricity demand in the learning database 115, generating, for each of the plurality of customer groups, a demand forecasting model 118 representing a relationship between the fluctuation factor data and electricity demand data based on the plurality of training data accumulated in the learning database 115, acquiring prediction data of the fluctuation factors for each of the plurality of customer groups, calculating prediction data of electricity demand for each of the plurality of customer groups based on the prediction data of the fluctuation factors and the demand forecasting model 118, and calculating prediction data of the total electricity demand for the retail electricity supplier based on the prediction data of electricity demand calculated for each of the plurality of customer groups. Below, this procedure is illustrated in detail by dividing it into a generation procedure of a demand forecasting model, a calculation procedure of prediction data of electricity demand, and a regeneration procedure of the demand forecasting model.

[0063] (Demand forecast model generation procedure) 7, the power demand forecasting system 10 first executes steps S01 and S02. In step S01, the performance information acquisition unit 111 acquires performance data on the fluctuation factors of power demand and corresponding performance data on power demand for each of a plurality of customer groups from the data collection system 20. In step S02, the encoding processing unit 113 checks whether the performance data on the fluctuation factors acquired by the performance information acquisition unit 111 includes a categorical variable.

[0064] When it is determined in step S02 that the performance data of the variation factors includes a categorical variable, the power demand forecasting system 10 executes step S03. In step S03, the encoding unit 113 converts the categorical variable included in the performance data of the variation factors into a quantitative variable by encoding processing.

[0065] Next, the power demand forecasting system 10 executes steps S04 and S05. If it is determined in step S02 that the performance data of the fluctuation factors does not include a categorical variable, the power demand forecasting system 10 executes steps S04 and S05 without executing step S03. In step S04, the data accumulation unit 114 accumulates the performance data of the fluctuation factors of the power demand acquired by the performance information acquisition unit 111 from the data collection system 20 and the corresponding performance data of the power demand in the learning database 115 for each of a plurality of customer groups. If the performance data of the fluctuation factors of the power demand includes a categorical variable, the data accumulation unit 114 accumulates the performance data of the fluctuation factors after the categorical variable is converted into a quantitative variable by the encoding processing unit 113 in the learning database 115. In step S05, the model generation unit 116 checks whether a learning command has been input by an input device or the like of the administrator terminal 30.

[0066] If it is determined in step S05 that the learning command has not been input, the power demand forecasting system 10 returns the process to step S01. Thereafter, until the learning command is input, accumulation of the teacher data for each of the multiple customer groups is repeated at a predetermined cycle.

[0067] If it is determined in step S05 that a learning command has been input, the power demand forecasting system 10 executes steps S06, S07, S08, and S09. In step S06, the model generation unit 116 selects one of the multiple customer groups as a target for generating a demand forecasting model. Hereinafter, in the description of steps S07 to S11, the customer group selected by the model generation unit 116 as a target for generating a demand forecasting model will be referred to as the "selected group."

[0068] In step S07, the model generation unit 116 extracts teacher data for the selected group from the teacher data stored in the learning database 115. In step S08, the model generation unit 116 generates a demand forecasting model 118 corresponding to the selected group by machine learning based on the extracted teacher data, and stores the model in the model holding unit 117. In step S09, the model generation unit 116 checks whether generation of demand forecasting models for all customer groups has been completed.

[0069] If it is determined in step S09 that there remains a customer group for which a demand forecasting model has not been generated, the power demand forecasting system 10 executes step S11. In step S11, the model generation unit 116 selects the next customer group from one or more customer groups for which a demand forecasting model has not been generated among the multiple customer groups. Thereafter, the power demand forecasting system 10 returns the process to step S07. Thereafter, the selection of a customer group and the generation of a demand forecasting model for the selected customer group are repeated until the generation of demand forecasting models for all customer groups is completed. If it is determined in step S09 that the generation of demand forecasting models for all customer groups is completed, the procedure for generating the demand forecasting model is completed.

[0070] (Calculation procedure for forecast data of electricity demand) As shown in FIG. 8, the power demand forecasting system 10 first executes steps S21, S22, and S23. In step S21, the input information acquisition unit 112 acquires forecast data of fluctuation factors of power demand for each of a plurality of customer groups. In step S22, the individual demand forecasting unit 121 selects one of the plurality of customer groups as a target for calculating forecast data. Hereinafter, the customer group selected by the individual demand forecasting unit 121 as a target for calculating forecast data is referred to as a "selected group." In step S23, the encoding processing unit 113 checks whether or not the forecast data of fluctuation factors for the selected group, among the forecast data of fluctuation factors acquired by the individual demand forecasting unit 121, includes a categorical variable.

[0071] When it is determined in step S23 that the prediction data of the variation factors for the selected group includes a categorical variable, the power demand prediction system 10 executes step S24. In step S24, the encoding unit 113 converts the categorical variable included in the prediction data of the variation factors for the selected group into a quantitative variable by encoding processing.

[0072] Next, the power demand forecasting system 10 executes steps S25 and S26. If it is determined in step S23 that the forecast data of the fluctuation factor for the selected group does not include a categorical variable, the power demand forecasting system 10 executes step S25 without executing step S24. In step S25, the individual demand forecasting unit 121 calculates forecast data of the power demand based on the forecast data of the fluctuation factor for the selected group and the demand forecast model 118 of the selected group stored in the model holding unit 117. If the forecast data of the fluctuation factor includes a categorical variable, the individual demand forecasting unit 121 calculates forecast data of the power demand based on the forecast data of the fluctuation factor after the categorical variable is converted into a quantitative variable by the encoding processing unit 113 and the demand forecasting model 118. In step S26, the individual demand forecasting unit 121 checks whether the calculation of the forecast data of the power demand for all customer groups is completed.

[0073] If it is determined in step S26 that there remains a customer group for which the forecast data of power demand has not been calculated, the power demand forecasting system 10 executes step S27. In step S27, the individual demand forecasting unit 121 selects the next customer group from one or more customer groups for which the forecast data of power demand has not been calculated among the multiple customer groups. Thereafter, the power demand forecasting system 10 returns the process to step S23. Thereafter, the selection of a customer group and the calculation of the forecast data of power demand for the selected customer group are repeated until the calculation of the forecast data of power demand for all customer groups is completed.

[0074] When it is determined in step S26 that the calculation of the power demand forecast data for all customer groups has been completed, the power demand forecasting system 10 executes step S31. In step S31, the forecast correction unit 133 checks whether or not a correction command for the power demand forecast data for any of the multiple customer groups has been input via the input device of the manager terminal 30.

[0075] When it is determined in step S31 that a correction command for the power demand forecast data has been input for any of the multiple customer groups, the power demand forecasting system 10 executes step S32. In step S32, the forecast correction unit 133 corrects new power demand forecast data based on the correction command input via the input device of the manager terminal 30.

[0076] Next, the power demand forecasting system 10 executes step S33. If it is determined in step S31 that a correction command for the forecast data of power demand has not been input for any customer group, the power demand forecasting system 10 executes step S33 without executing step S32. In step S33, the total demand forecasting unit 122 checks whether or not a command for outputting forecast data of total power demand has been input via an input device of the manager terminal 30.

[0077] If it is determined in step S33 that a command to output forecast data of the total power demand has not been input, the power demand forecasting system 10 returns the process to step S31. Thereafter, until a command to output forecast data of the total power demand is input, the power demand forecasting system 10 repeats correcting new forecast data of the power demand in response to the input of a correction command. Note that, in step S37 described later, since the error evaluation result is not visualized until the first evaluation image 140 is displayed, at least in the first cycle, step S32 is not executed.

[0078] If it is determined in step S33 that a command to output forecast data of total power demand has been input, power demand forecasting system 10 executes steps S34, S35, S36, and S37. In step S34, total demand forecasting unit 122 calculates forecast data of total power demand for the electricity retailer based on the forecast data of power demand calculated for each of multiple customer groups. In step S35, forecast result output unit 123 outputs the forecast data of total power demand calculated by total demand forecasting unit 122 to data collection system 20.

[0079] In step S36, the performance information acquisition unit 111 acquires, for each of a plurality of customer groups, performance data on the fluctuation factors of electricity demand and corresponding performance data on electricity demand from the data collection system 20. In step S37, the encoding processing unit 113 checks whether the performance data on the fluctuation factors acquired by the performance information acquisition unit 111 includes a categorical variable.

[0080] When it is determined in step S37 that the performance data of the variation factors includes a categorical variable, the power demand forecasting system 10 executes step S38. In step S38, the encoding unit 113 converts the categorical variable included in the performance data of the variation factors into a quantitative variable by encoding processing.

[0081] Next, the power demand forecasting system 10 executes steps S39, S41, and S42. If it is determined in step S37 that the performance data of the fluctuation factors does not include a categorical variable, the power demand forecasting system 10 executes steps S39, S41, and S42 without executing step S38. In step S39, the data accumulation unit 114 accumulates the performance data of the fluctuation factors of the power demand acquired by the performance information acquisition unit 111 from the data collection system 20 and the corresponding performance data of the power demand in the learning database 115 for each of a plurality of customer groups. If the performance data of the fluctuation factors of the power demand includes a categorical variable, the data accumulation unit 114 accumulates the performance data of the fluctuation factors after the categorical variable is converted into a quantitative variable by the encoding processing unit 113 in the learning database 115.

[0082] In step S41, the error evaluation unit 131 evaluates the error between the forecast data of power demand and the actual data of power demand for each of the multiple customer groups. In step S42, the evaluation image display unit 132 displays an evaluation image that visualizes the evaluation result of the error for each of the multiple customer groups on a monitor. For example, the evaluation image display unit 132 displays a first evaluation image 140 and a second evaluation image 150 on the monitor of the administrator terminal 30.

[0083] Thereafter, the power demand forecasting system 10 returns the process to step S21. After that, the first evaluation image 140 and the second evaluation image 150 are displayed on the monitor of the manager terminal 30, and the error evaluation result is visualized. This makes it possible to input a correction command for the power demand forecast data through the input device of the manager terminal 30, for example, based on the first evaluation image 140. Therefore, step S32 can be executed in response to the input of the correction command.

[0084] (Procedure for regenerating demand forecast model) As shown in Fig. 9, the electricity demand forecasting system 10 executes steps S51, S52, S53, and S54 in order. In step S51, the model correction unit 134 waits for a command to correct the demand forecasting model to be input by the input device of the manager terminal 30 based on the second evaluation image 150. In step S52, the model correction unit 134 requests the model generation unit 116 to regenerate a demand forecasting model for the customer group for which the command to correct the demand forecasting model has been input. Hereinafter, the customer group for which the command to correct the demand forecasting model has been input is referred to as the "group to be corrected."

[0085] In step S53, the model generation unit 116 extracts teacher data for the correction target group from the teacher data accumulated in the learning database 115. In step S54, the model generation unit 116 regenerates the demand forecasting model 118 corresponding to the correction target group by machine learning based on the extracted teacher data, and stores the regenerated demand forecasting model 118 in the model holding unit 117. Thereafter, the power demand forecasting system 10 returns the process to step S51.

[0086] [Effects of this embodiment] As described above, the electricity demand forecasting system 10 includes a data storage unit 114 that accumulates, for each of the multiple customer groups of the electricity seller, training data including actual data on electricity demand fluctuation factors and actual data on electricity demand in a learning database 115; a model generation unit 116 that generates, for each of the multiple customer groups, a demand forecast model 118 that represents the relationship between the fluctuation factor data and electricity demand data based on the multiple training data accumulated in the learning database 115; an input information acquisition unit 112 that acquires predicted data of the fluctuation factors for each of the multiple customer groups; an individual demand forecasting unit 121 that calculates predicted data of electricity demand for each of the multiple customer groups based on the predicted data of the fluctuation factors and the demand forecasting model 118; and a total demand forecasting unit 122 that calculates predicted data of total electricity demand for the electricity seller based on the predicted data of electricity demand calculated for each of the multiple customer groups.

[0087] Factors that affect power demand may vary greatly depending on the customer. For example, for customers who consume power in offices, weather and the like tend to be major factors, whereas for customers who consume power in production plants, production conditions tend to be major factors. In response to this, the power demand forecasting system 10 generates a demand forecasting model 118 for each of a plurality of customer groups, calculates forecast data of power demand for each of the plurality of customer groups based on the demand forecasting model 118, and calculates forecast data of total power demand for the power seller based on the forecast data of power demand calculated for each of the plurality of customer groups. For this reason, it is possible to group customers according to differences in factors that affect power demand, forecast the power demand for each group with high accuracy, and aggregate the forecast results to forecast the total power demand with high accuracy. This is therefore effective in improving the accuracy of forecasting power demand.

[0088] The model generation unit 116 may generate a demand forecast model by at least a random forest method. In this case, a demand forecast model with high forecast accuracy can be easily generated.

[0089] The model generation unit 116 may generate a demand forecasting model by ensemble learning that combines the random forest method with one or more other learning methods. In this case, a demand forecasting model with higher prediction accuracy can be generated.

[0090] The one or more other learning techniques may include at least a recurrent neural network method, which may generate a demand forecasting model with higher forecasting accuracy.

[0091] The multiple customer groups may include a first group that consumes electricity in an office and a second group that consumes electricity in a production factory, and the model generation unit 116 may generate a first demand forecast model representing the relationship between the electricity demand data and the variable factor data including at least the weather for the first group, and generate a second demand forecast model representing the relationship between the electricity demand data and the variable factor data including at least the production status for the second group. In this case, the customers are divided into groups according to differences in factors that affect electricity demand, and the electricity demand for each group is predicted with high accuracy, and the prediction results are compiled to predict the total electricity demand with high accuracy.

[0092] The number of customers included in the first group may be greater than the number of customers included in the second group. There is a warning that fluctuation factors of electricity demand tend to differ between customers who consume electricity in production factories. On the other hand, fluctuation factors of electricity demand tend to be similar between customers who consume electricity in offices. For this reason, by grouping a larger number of customers into the first group than the number of customers included in the second group, the reliability of the training data can be improved, and the prediction accuracy of the first demand forecasting model can be improved.

[0093] The multiple customer groups may further include a third group that consumes electricity in a production factory that produces products different from those produced by the second group, and the model generation unit 116 may generate a third demand forecasting model that represents the relationship between the fluctuation factor data, including at least the production status, and the electricity demand data for the third group. In this case, by grouping customers that produce different products, the forecasting accuracy of the demand forecasting model 118 for each group can be improved.

[0094] The variable factor data including the production status may include data that quantifies the production status by encoding. In this case, it is possible to more easily generate the demand forecast model 118 for a group including customers who consume electricity in a production plant.

[0095] The encoding process may include a target encoding process in which the magnitude of the power demand is used as a response variable, which can further improve the prediction accuracy of the power demand forecast model for a group including customers who consume power in a production plant.

[0096] The power demand forecasting system 10 may further include an error evaluation unit 131 that evaluates an error between forecast data of power demand and actual data of power demand for each of a plurality of customer groups, and a forecast correction unit 133 that corrects new forecast data of power demand based on the error evaluation result for each of a plurality of customer groups. While it is difficult to obtain reproducibility of the error between forecast data and actual data for the total power demand, it is easy to obtain reproducibility of the error between forecast data and actual data for each customer group. Therefore, by performing error evaluation and correction of the forecast data of power demand based on the error evaluation result for each of a plurality of customer groups, it is possible to further improve the forecast accuracy of power demand.

[0097] The power demand forecasting system 10 further includes an evaluation image display unit 132 that displays an evaluation image that visualizes the evaluation result of the error on a monitor for each of the multiple customer groups, and the forecast correction unit 133 may correct new forecast data of power demand for each of the multiple customer groups based on a user input after the display of the evaluation image. In this case, by visualizing the evaluation result of the error for each of the multiple customer groups and leaving it up to the user to correct the forecast data of power demand for each of the multiple customer groups, it is possible to effectively utilize the user's knowledge to generate forecast data of power demand.

[0098] The evaluation image display unit 132 may display on the monitor a first evaluation image that visualizes the change over time of the evaluation result of the error for each of the multiple customer groups. In this case, the recurrence tendency of the error is displayed for each of the multiple customer groups, and it is possible to encourage the user to make more appropriate corrections to the power demand forecast data.

[0099] The evaluation image display unit 132 may display on the monitor a first evaluation image 140 in which colors representing the evaluation results of the error are mapped according to a plurality of customer groups and the time of evaluation. In this case, the recurrence tendency of the error can be more clearly shown for each of the plurality of customer groups.

[0100] The power demand forecasting system 10 may further include an error evaluation unit 131 that evaluates an error between forecast data of power demand and actual data of power demand for each of a plurality of customer groups, and a model correction unit 134 that corrects the demand forecasting model 118 based on the error evaluation result for each of a plurality of customer groups. The prediction accuracy of the demand forecasting model 118 may be reduced due to changes in the power facilities of the customer. For example, the prediction accuracy of the demand forecasting model 118 may be reduced due to the customer's expansion of private power generation facilities, expansion of machine tools, and an increase in the ratio of unmanned processes. By performing the error evaluation and the correction of the demand forecasting model based on the error evaluation result for each of a plurality of customer groups, the demand forecasting model can be corrected more appropriately at a more appropriate time. This makes it easy to maintain high prediction accuracy of power demand.

[0101] The electricity demand forecasting system 10 may further include an evaluation image display unit 132 that displays an evaluation image that visualizes the evaluation result of the error on a monitor for each of the multiple customer groups, and the model correction unit 134 may correct the demand forecasting model 118 for each of the multiple customer groups based on a user input after the evaluation image is displayed. In this case, by visualizing the evaluation result of the error for each of the multiple customer groups and leaving the correction of the demand forecasting model 118 for each of the multiple customer groups to the user, the demand forecasting model 118 can be more appropriately corrected by effectively utilizing the user's knowledge.

[0102] The evaluation image display unit 132 may display on the monitor a second evaluation image that visualizes the relationship between the error evaluation result and the correction criterion for the demand forecasting model 118 for each of the multiple customer groups. In this case, the judgment criterion for whether or not to correct the demand forecasting model 118 can be presented in an easy-to-understand manner for each of the multiple customer groups.

[0103] The evaluation image display unit 132 may display on the monitor, for each of the multiple customer groups, a second evaluation image 150 including a bar graph 151 showing the evaluation result of the error and a correction reference line 152 showing the correction reference for the demand forecasting model 118. In this case, the judgment criteria for whether or not to correct the demand forecasting model 118 can be presented for each of the multiple customer groups in an easier-to-understand manner.

[0104] Although the embodiments have been described above, the present disclosure is not necessarily limited to the above-described embodiments, and various modifications are possible without departing from the gist of the present disclosure. [Explanation of symbols]

[0105] 10...electricity demand forecasting system, 112...input information acquisition unit, 114...data storage unit, 115...learning database (database), 116...model generation unit, 118...demand forecasting model, 121...individual demand forecasting unit, 122...total demand forecasting unit, 131...error evaluation unit, 132...evaluation image display unit, 133...forecast correction unit, 134...model correction unit, 140...first evaluation image (evaluation image), 150...second evaluation image (evaluation image), 151...bar graph, 152...corrected reference line.

Claims

1. a data storage unit that stores, in a database, teacher data including actual data on fluctuation factors of power demand and actual data on power demand for each of a plurality of customer groups of the power selling company; a model generation unit that generates, for each of the plurality of customer groups, a demand forecast model that represents a relationship between fluctuation factor data and electricity demand data based on a plurality of teacher data stored in the database; an input information acquisition unit that acquires prediction data of the fluctuation factors for each of the plurality of customer groups; an individual demand forecasting unit that calculates forecast data of electricity demand for each of the plurality of customer groups based on the forecast data of the fluctuation factors and the demand forecast model; a total demand forecasting unit that calculates forecast data of a total electricity demand for the electricity seller based on the forecast data of the electricity demand calculated for each of the plurality of customer groups, The model generation unit generating an electricity demand forecasting model representing a relationship between fluctuation factor data including one fluctuation factor and electricity demand data for one customer group; An electricity demand forecasting system that generates an electricity demand forecasting model for other customer groups, the electricity demand forecasting model representing the relationship between variable factor data including other variable factors not included in the variable factor data for the one customer group and electricity demand data.

2. The power demand forecasting system according to claim 1 , wherein the model generation unit generates the demand forecasting model using at least a random forest method.

3. The power demand forecasting system according to claim 2 , wherein the model generation unit generates the demand forecasting model by ensemble learning that combines the random forest method with one or more other learning methods.

4. The power demand forecasting system according to claim 3 , wherein the one or more other learning techniques include at least a recurrent neural network method.

5. the plurality of customer groups include a first group that consumes electricity in an office and a second group that consumes electricity in a production factory; The model generation unit generating a first demand forecasting model representing a relationship between variable factor data including at least weather and power demand data for the first group; The power demand forecasting system according to any one of claims 1 to 4, further comprising: a second demand forecasting model that represents a relationship between variable factor data including at least production status and power demand data for the second group.

6. The power demand forecasting system according to claim 5 , wherein the number of customers included in the first group is greater than the number of customers included in the second group.

7. the plurality of customer groups further includes a third group that consumes electricity in a production factory that produces products different from those produced in the production factory in which the second group consumes electricity; The power demand forecasting system according to claim 5 or 6, wherein the model generation unit generates a third demand forecasting model representing a relationship between the power demand data and variable factor data including at least a production status for the third group.

8. The power demand forecasting system according to any one of claims 5 to 7, wherein the fluctuation factor data including the production status includes data that digitizes the production status by encoding processing.

9. The power demand forecasting system according to claim 8 , wherein the encoding process includes a target encoding process in which the magnitude of the power demand is used as a response variable.

10. an error evaluation unit that evaluates an error between the power demand forecast data and the power demand actual data for each of the plurality of customer groups; The power demand forecasting system according to any one of claims 1 to 9, further comprising: a forecast correction unit that corrects new forecast data of power demand for each of the plurality of customer groups based on a result of the error evaluation.

11. The power demand forecasting system according to claim 10 , further comprising an evaluation image display unit that displays, on a monitor, an evaluation image that visualizes an evaluation result of the error for each of the plurality of customer groups.

12. The power demand forecasting system according to claim 11 , wherein the forecast correction unit corrects the new forecast data of the power demand for each of the plurality of customer groups based on a user input after the evaluation image is displayed.

13. The power demand forecasting system according to claim 12 , wherein the evaluation image display unit displays, on the monitor, the evaluation image that visualizes a change over time in the evaluation result of the error for each of the plurality of customer groups.

14. The power demand forecasting system according to claim 13 , wherein the evaluation image display unit displays on the monitor the evaluation image in which colors representing the evaluation results of the errors are mapped according to the multiple customer groups and the evaluation time.

15. an error evaluation unit that evaluates an error between the power demand forecast data and the power demand actual data for each of the plurality of customer groups; The power demand forecasting system according to any one of claims 1 to 9, further comprising: a model correction unit that corrects the demand forecasting model based on the error evaluation result for each of the plurality of customer groups.

16. The power demand forecasting system according to claim 15 , further comprising an evaluation image display unit that displays, on a monitor, an evaluation image that visualizes an evaluation result of the error for each of the plurality of customer groups.

17. The power demand forecasting system according to claim 16 , wherein the model correction unit corrects the demand forecast model for each of the plurality of customer groups based on a user input after the evaluation image is displayed.

18. The electricity demand forecasting system of claim 17, wherein the evaluation image display unit displays on the monitor the evaluation image that visualizes the relationship between the error evaluation result and the correction criterion for the demand forecasting model for each of the multiple customer groups.

19. The power demand forecasting system of claim 18, wherein the evaluation image display unit displays on the monitor the evaluation image including a bar graph representing the evaluation result of the error and a correction reference line representing the correction criterion for the demand forecasting model for each of the multiple customer groups.

20. accumulating, in a database, teacher data including actual data on fluctuation factors of power demand and actual data on power demand for each of a plurality of customer groups of the power selling company; generating a demand forecasting model representing a relationship between fluctuation factor data and electricity demand data for each of the plurality of customer groups based on a plurality of teacher data stored in the database; acquiring prediction data of the fluctuation factors for each of the plurality of customer groups; Calculating, for each of the plurality of customer groups, forecast data of electricity demand based on the forecast data of the fluctuation factors and the demand forecast model; calculating forecast data of total electricity demand for the electricity seller based on the forecast data of electricity demand calculated for each of the plurality of customer groups; Generating the demand forecast model includes: generating an electricity demand forecasting model representing a relationship between fluctuation factor data including one fluctuation factor and electricity demand data for one customer group; generating an electric power demand forecasting model representing a relationship between electric power demand data and fluctuation factor data including other fluctuation factors not included in the fluctuation factor data for the one customer group for another customer group; The power demand forecasting method includes:

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