Model generation device, prediction device, model generation method, and prediction method

Abstract

To enhance prediction accuracy of demand power amount in a facility.SOLUTION: A model generation device (10) sets a plurality of lags with respect to actual values, about time series data indicating a time transition of the actual values of a demand power amount in a facility, thereby, deriving a self-correlation coefficient of the actual value corresponding to each of the lags, then, on the basis of the self-correlation coefficient, selects a specific lag out of the lags, sets the specific lag with respect to the actual value for deriving a new feature amount, and on the basis of the time series data to which the new feature amount is added, generates a prediction model for predicting the demand power amount in the facility, by machine learning.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] One aspect of the present invention relates to a model generating device that generates a prediction model for predicting an amount of power demand in a facility. [Background technology]

[0002] In recent years, machine learning has been used to predict various types of energy demand in facilities. For example, Patent Document 1 below discloses a technology for predicting the heat load in a thermal storage utilization system by using a neural network. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 9-89348 Summary of the Invention [Problem to be solved by the invention]

[0004] An aspect of the present invention aims to improve the accuracy of predicting the amount of power demand in a facility. [Means for solving the problem]

[0005] In order to solve the above problem, a model generation device according to one embodiment of the present invention is a model generation device that generates a prediction model for predicting an amount of electricity demand in a facility, wherein the model generation device sets multiple lags for time series data indicating a time progression of an actual value of the amount of electricity demand, derives autocorrelation coefficients of the actual value corresponding to each of the multiple lags, selects a special lag from among the multiple lags based on the autocorrelation coefficients, derives a new feature by setting the special lag for the actual value, and generates the prediction model by machine learning based on the time series data to which the new feature has been added.

[0006] Moreover, a prediction device according to one aspect of the present invention is a prediction device that derives a predicted value of an amount of power demand by using, in a prediction phase, a prediction model for predicting an amount of power demand in a facility, which has been generated in advance in a learning phase. In the learning phase, for time series data in the learning phase showing a time transition of an actual value of the amount of power demand, a plurality of lags are set for the actual value, thereby deriving an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags, a special lag is selected from among the plurality of lags based on the autocorrelation coefficient, a new feature is derived by setting the special lag for the actual value, and the prediction model is generated by machine learning based on the time series data to which the new feature has been added, and the prediction device derives a new feature for the prediction phase by setting the special lag for the time series data in the prediction phase showing a time transition of the actual value, and outputs the predicted value to the prediction model based on the time series data in the prediction phase to which the new feature has been added.

[0007] Furthermore, a model generation method according to one aspect of the present invention is a model generation method for generating a prediction model for predicting an amount of electricity demand in a facility, and includes the steps of: setting multiple lags for actual values ​​of time series data indicating a time progression of the actual values ​​of the amount of electricity demand in the facility, thereby deriving an autocorrelation coefficient of the actual values ​​for each of the multiple lags; selecting a special lag from among the multiple lags based on the autocorrelation coefficients; deriving a new feature by setting the special lag for the actual value; and generating the prediction model by machine learning based on the time series data to which the new feature has been added.

[0008] In addition, a prediction method according to one aspect of the present invention is a prediction method for deriving a predicted value of an amount of power demand by using a prediction model for predicting an amount of power demand in a facility, which has been generated in advance in a learning phase, in a prediction phase. In the learning phase, for time series data in the learning phase showing a time transition of an actual value of the amount of power demand, a plurality of lags are set for the actual value, thereby deriving an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags, a special lag is selected from among the plurality of lags based on the autocorrelation coefficient, a new feature is derived by setting the special lag for the actual value, and the prediction model is generated by machine learning based on the time series data to which the new feature has been added, and the prediction method includes a step of deriving a new feature for the prediction phase by setting the special lag for the time series data in the prediction phase showing a time transition of the actual value, and a step of causing the prediction model to output the predicted value based on the time series data in the prediction phase to which the new feature has been added. Effect of the Invention

[0009] According to one aspect of the present invention, it is possible to improve the accuracy of predicting the amount of power demand in a facility. [Brief description of the drawings]

[0010] [Figure 1] 1 is a block diagram illustrating a configuration of a main part of an information processing device according to a first embodiment. [Diagram 2] 1 is an example of performance data as training data. [Diagram 3] 1 is an example of workday data as training data. [Figure 4] 4 is an example of data generated by applying pre-interpolation to the workday data of FIG. 3. [Diagram 5]5 is an example of data generated by combining the performance data of FIG. 2 with the data of FIG. 4. [Figure 6] 13 is an example of a table showing a correspondence relationship between lags and ACs. [Figure 7] 1 is an example of a graph showing the relationship between lag and AC. [Figure 8] FIG. 6 is an example of data generated by adding lag features to the data in FIG. 5. [Figure 9] 9 is an example of data generated by adding a rolling feature component and a rolling feature to the data of FIG. 8. [Figure 10] FIG. 10 is an example of data generated by removing the rolling feature component from the data in FIG. [Figure 11] FIG. 11 is an example of data generated by adding a time point equivalent to the data of FIG. 10. [Figure 12] 12 is an example of data generated by adding days of the week to the data in FIG. 11. [Figure 13] 13 is an example of data generated by standardizing each feature amount in the data of FIG. 12. [Figure 14] 13 is a flowchart illustrating a process of selecting an arithmetic progression element. [Figure 15] 1 is an example of pre-processed input data in the prediction phase. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0011] [Embodiment 1] The first embodiment will be described below. For convenience of explanation, components having the same functions as those described in the first embodiment will be given the same reference numerals in the following embodiments, and the explanations will not be repeated. For brevity, the explanations of known technical matters will be omitted as appropriate. All components and numerical values ​​described in this specification are merely examples unless otherwise stated. Therefore, for example, unless otherwise stated, the positional relationship and connection relationship of each component are not limited to the examples in each figure. In addition, please note that the correspondence between the date and the day of the week in the examples in each figure does not necessarily match that of an actual calendar. In this specification, unless otherwise stated, the notation "A~B" for two numbers A and B means "greater than or equal to A and less than or equal to B."

[0012] (Overview of information processing device 1) 1 is a block diagram illustrating a configuration of a main part of an information processing device 1. The information processing device 1 may include a control device 9 and a storage unit 90. The control device 9 comprehensively controls each part of the information processing device 1. The control device 9 may include a model generating device 10 and a prediction device 20.

[0013] As described below, the model generation device 10 generates a prediction model for predicting the amount of power demand in a facility by machine learning. For this reason, the model generation device 10 may be called a learning device. The prediction device 20 derives a prediction value of the amount of power demand in the facility by using the prediction model generated in advance by the model generation device 10.

[0014] The facility according to one embodiment of the present invention may be a facility in which the demand for electric power may fluctuate depending on at least one of the time of day, the day of the week, and the working status of the employees in the facility. Examples of the facility according to one embodiment of the present invention include industrial facilities such as factories, commercial facilities such as shopping malls, and medical facilities such as hospitals.

[0015] The memory unit 90 stores various data and programs used for processing by the control device 9. In the example of Fig. 1, it is assumed that actual data and workday data are stored in the memory unit 90. In this specification, actual data collectively refers to data (more specifically, data structure) indicating actual values ​​of the amount of power demand in a facility, recorded with a predetermined time resolution. In this specification, unless otherwise contradictory, the term "amount of power demand" refers to the actual value of the amount of power demand.

[0016] The actual data is data in which a time point and an amount of power demand are associated with each other, and is therefore an example of time-series data that indicates the time transition of the amount of power demand. In the example of the first embodiment, the time resolution of the actual data is one minute. In the first embodiment, the time point in data with a time resolution of one minute is expressed in a "year / month / date hour / minute" format ("yyyy / mm / dd / hh / mm" format). The time point may be used as a key in the data structure.

[0017] In this specification, workday data collectively refers to data indicating the daily working status in a facility. As an example, workday data may be data indicating whether a certain day is a working day in the facility. Workday data is data in which dates and working statuses are associated with each other, and is therefore an example of time-series data indicating daily changes in working status. In the example of the first embodiment, the time resolution of the workday data before the pre-interpolation described below is one day. In the first embodiment, the date in data with a time resolution of one day is expressed in the "year / month / day" format ("yyyy / mm / dd" format).

[0018] (Learning Phase) The following describes an example of the operation of the information processing device 1. The processing of the information processing device 1 is roughly divided into processing in a learning phase (a series of processing by the model generating device 10) and processing in a prediction phase (a series of processing by the prediction device 20). First, an example of the learning phase will be described.

[0019] 1, the model generating device 10 may include a data acquiring unit 11, a preprocessing unit 12, a learning unit 13, and an evaluation unit 14. The data acquiring unit 11 may be referred to as a learning-time data acquiring unit or a first data acquiring unit. The preprocessing unit 12 may be referred to as a learning-time preprocessing unit or a first preprocessing unit.

[0020] As shown in Fig. 1, the storage unit 90 may store performance data DZ and workday data DS used in the learning phase. Hereinafter, for example, the performance data DZ will be simply abbreviated as DZ. The data acquisition unit 11 acquires DZ and DS as input data sets in the learning phase. Then, the data acquisition unit 11 divides the input data set into training data and validation data.

[0021] DZ0 in FIG. 2 is an example of actual data as training data. DZ0 is a part of DZ. In the example of embodiment 1, the unit of demand energy recorded in the actual data is kWh. DS0 in FIG. 3 is an example of workday data as training data. DS0 is a part of DS. In the example of embodiment 1, the working status recorded in the workday data is converted in advance into a qualitative variable called "workday" (more specifically, a nominal scale). In the example of FIG. 3, a workday value of "1" indicates that the day (the day corresponding to the workday) is a workday. On the other hand, a workday value of "0" indicates that the day is a holiday.

[0022] However, the type of working status recorded in the workday data is not limited to the above example. For example, a type of working status called "long vacation day" may be recorded in the workday data. A long vacation day means a holiday that belongs to a specified long vacation (e.g., summer vacation period). In this case, for example, a value of "-1" for the workday may indicate that the day is a long vacation day.

[0023] The preprocessing unit 12 generates preprocessed training data by performing a series of preprocessing processes on the training data prior to execution of the machine learning algorithm by the learning unit 13. Next, the learning unit 13 executes the machine learning algorithm to generate a prediction model based on the preprocessed training data.

[0024] First, the pre-processing unit 12 generates new data by performing pre-interpolation on the workdays recorded in DS0. DS1 in FIG. 4 is an example of the new data. DS1 may be referred to as pre-interpolated workday data. Specifically, the pre-processing unit 12 converts DS0 into new data having a time resolution (e.g., 1 minute) equal to that of DZ0 by pre-interpolation. As shown in FIG. 4, as a result of pre-interpolation, workdays belonging to the same date have the same value at all time points in DS1.

[0025] Next, the preprocessing unit 12 generates new data by combining DZ0 and DS1 using the time point as a key. DZ1 in FIG. 4 is an example of the new data. For example, the preprocessing unit 12 generates DZ1 by adding a column of "workday" in DS1 to DZ0. DZ1 may be referred to as actual data with workday data.

[0026] Next, the preprocessing unit 12 calculates an autocorrelation coefficient of the demand energy in the DZ1. In this specification, the autocorrelation coefficient is abbreviated as AC. Specifically, the preprocessing unit 12 changes the lag in various ways and calculates the AC for each lag. The lag represents a time delay amount set for the feature amount as time-series data.

[0027] The pre-processing unit 12 may set the lag as a product of the lag unit amount and the lag order. Then, the pre-processing unit 12 may calculate AC for each determined lag. As an example, the lag unit amount may be a fixed value preset by a user of the information processing device 1. In the example of the first embodiment, a case where the lag unit amount is 30 minutes is illustrated. However, the lag unit amount may be set by the pre-processing unit 12. For example, the pre-processing unit 12 may set the time resolution of DZ1 (e.g., 1 minute) as the lag unit amount.

[0028] The preprocessing unit 12 may generate a table showing the correspondence between lags and ACs. LT in FIG. 6 shows an example of the table. In the example of FIG. 6, the lag order may be used as a key in LT. In the example of FIG. 6, the preprocessing unit 12 sets 1008 lags by changing the lag order from 1 to 1008. Therefore, the preprocessing unit 12 calculates 1008 ACs. The lag in the example of the first embodiment is an amount representing a time length in days.

[0029] As mentioned above, the lag unit amount is 30 minutes, so a lag order of 1 corresponds to 30 minutes. Therefore, a lag order of 48 corresponds to 1 day (=30 minutes x 48). And a lag order of 336 (=48 x 7) corresponds to 7 days, or 1 week. And a lag order of 672 (=336 x 2) corresponds to 14 days, or 2 weeks, and a lag order of 1008 (=336 x 3) corresponds to 21 days, or 3 weeks.

[0030] In this specification, the element number of the data structure is generally represented by an integer i. In the example of FIG. 6, i may be equal to the lag order. Therefore, in the example of FIG. 6, the lag corresponding to i is represented as lag[i]. Also, the AC corresponding to i is represented as AC[i]. As is clear from the above description, lag[i] for i>1 represents the i-th shortest lag. In this specification, unless otherwise contradicted, the minimum value of the element number of the data structure is 0 according to the notation of a general programming language. Although not shown in FIG. 6, lag[0]=0 and AC[0]=1, as will be clear to those skilled in the art.

[0031] The preprocessing unit 12 selects (extracts) a special lag from among the multiple lags that have been set based on the AC. As an example, the preprocessing unit 12 may select, as the special lag, a lag that has a value equal to or greater than a time length threshold and corresponds to an AC that has a value equal to or greater than an autocorrelation threshold among the multiple lags.

[0032] Generally, in the field of machine learning, when the correlation coefficient between two data is 0.7 or more, it can be considered that a strong positive correlation exists between the data. Therefore, as an example, the autocorrelation threshold may be 0.7 to 1. The autocorrelation threshold may be a value that can be arbitrarily set by the user. In the first embodiment, a case where the autocorrelation threshold is 0.7 is illustrated.

[0033] Fluctuations in the amount of power demand in a facility are generally considered to have a cycle of about one day in the short term. Therefore, the time length threshold may be set to a value that is considered appropriate for grasping the fluctuation trend of the amount of power demand in a relatively short term. The time length threshold may also be a value that can be arbitrarily set by the user. In the first embodiment, a case where the time length threshold is one day is illustrated as an example.

[0034] As will be clear to those skilled in the art, when the lag is short, an AC close to 1 is likely to occur. Therefore, by setting the time length threshold as described above, it is possible to prevent many short lags from being selected as special lags. In other words, it is possible to reduce the number of special lags selected (see the rows of lag orders 1 to 3 in the example of FIG. 6). As a result, as will be understood from the explanation below, it is possible to improve the quality of the prediction model.

[0035] As mentioned above, the time length threshold of 1 day corresponds to i=48. In the example of FIG. 6, in the range of i≧48, AC

[48] =0.78 AC

[0336] =0.73 AC

[0672] =0.70 AC

[1008] =0.72 has a value equal to or greater than the autocorrelation threshold. Therefore, the preprocessing unit 12 lag

[48] =1 lag

[0336] =7 lag

[0672] =14 lag

[1008] =21 is chosen as the special lag.

[0036] The inventors used performance data obtained from a certain factory to derive the relationship between lag and AC in that factory. FIG. 7 is a graph showing the relationship between lag and AC in that factory derived by the inventors. In the graph of FIG. 7, the horizontal axis indicates the lag degree, and the vertical axis indicates AC. The horizontal axis in the graph of FIG. 7 can be read as lag. The trend of the graph of FIG. 7 roughly coincides with the example of FIG. 6.

[0037] Next, the pre-processing unit 12 may sort the extracted multiple special lugs in ascending order. Then, the pre-processing unit 12 may generate an array Plag including the sorted special lugs as elements. In the example of the first embodiment, the pre-processing unit 12 generates Plag=[1, 7, 14, 21]. Then, the pre-processing unit 12 selects elements that form an arithmetic progression (arithmetic progression elements) from among the elements of Plag. The method of selecting the arithmetic progression elements will be described later.

[0038] In the example of the first embodiment, the preprocessing unit 12 selects the first and subsequent elements of Plag, Plag[1:]=[7,14,21], as arithmetic progression elements. These arithmetic progression elements form an arithmetic progression whose first term has a value of 7 and whose common difference is 7.

[0039] Then, the preprocessing unit 12 selects elements of Plag excluding the arithmetic progression elements as the remaining elements. The remaining elements may be called non-arithmetic progression elements (elements that do not form an arithmetic progression). In the example of the first embodiment, the processing unit 12 selects the 0th element, Plag[0]=[1], as the remaining element of Plag.

[0040] The pre-processing unit 12 derives a new feature by setting the special lag selected as described above to the power demand. For example, the pre-processing unit 12 may derive at least one of a rolling feature and a lag feature as the new feature.

[0041] Considering the above-mentioned FIG. 7, it is considered that the special lag corresponding to the arithmetic progression element is suitable for expressing the relatively long-term periodicity of the fluctuation in the demand energy. Therefore, the pre-processing unit 12 may select the special lag corresponding to the arithmetic progression element as the rolling lag, which is the special lag for deriving the rolling feature. In the example of the first embodiment, the pre-processing unit 12 selects the lags "7, 14, and 21" as the rolling lags. In the example of the first embodiment, the pre-processing unit 12 derives the rolling feature based on the rolling lags.

[0042] Then, the pre-processing unit 12 may select the special lag corresponding to the remaining element as a non-rolling lag, which is a special lag for deriving a feature different from the rolling feature. In the example of the first embodiment, the pre-processing unit 12 selects the lag "1" as the non-rolling lag. The non-rolling lag is considered to be suitable for expressing the relatively short-term periodicity of fluctuations in the amount of power demand. Therefore, in the example of the first embodiment, the pre-processing unit 12 derives a lag feature based on the non-rolling lag.

[0043] In the first embodiment, a case where a lag feature is derived before a rolling feature is derived will be illustrated. However, as will be clear to those skilled in the art, a rolling feature may be derived before a lag feature is derived. The processes in the first embodiment may be performed in any order unless there is a particular contradiction.

[0044] The preprocessing unit 12 derives a lag feature as a new feature corresponding to the demand power amount by setting a non-rolling lag for the demand power amount. DZ2 in FIG. 8 shows an example of data generated by adding a lag feature to DZ1. In the example of the first embodiment, since the non-rolling lag is 1, the preprocessing unit 12 derives the "demand power amount one day ago" as the lag feature. Therefore, in the example of FIG. 8, the "demand power amount one day ago" belonging to the row of the time point "2021 / 4 / 1 0:00" represents the "demand power amount at 2021 / 3 / 31 0:00". In addition, the "demand power amount one day ago" belonging to the row of the time point "2022 / 3 / 31 23:59" represents the "demand power amount at 2022 / 3 / 30 23:59".

[0045] Next, the preprocessing unit 12 derives a rolling feature as a new feature corresponding to the demand power amount by setting a non-rolling lag for the demand power amount. First, the preprocessing unit 12 acquires the demand power amount at a time point that is the rolling lag before a certain time point as a constituent element (rolling feature component) of the rolling feature corresponding to the time point. In the example of the first embodiment, since the rolling lags are 7, 14, and 21, the preprocessing unit 12 acquires the "demand power amount 7 days ago", the "demand power amount 14 days ago", and the "demand power amount 21 days ago" as the rolling feature components. The preprocessing unit 12 adds the rolling feature components to the DZ2.

[0046] Next, the preprocessing unit 12 derives a statistical value of the rolling feature component as a rolling feature. In the first embodiment, a case where the statistical value is an average value is illustrated. Therefore, the preprocessing unit 12 derives an average value of the "demand energy amount 7 days ago", the "demand energy amount 14 days ago", and the "demand energy amount 21 days ago". The preprocessing unit 12 further adds the average value to the DZ2 as a rolling feature.

[0047] DZ3 in FIG. 9 shows an example of data generated by adding a rolling feature component and a rolling feature to DZ2. In the example of FIG. 9, the "demand energy amount 7 days ago", "demand energy amount 14 days ago", and "demand energy amount 21 days ago" belonging to the row of the time point "2021 / 4 / 1 0:00" represent "demand energy amount at 2021 / 3 / 25 0:00", "demand energy amount at 2021 / 3 / 18 0:00", and "demand energy amount at 2021 / 3 / 11 0:00", respectively. And the "average (7, 14, 21 days ago) demand energy amount" belonging to the row of the time point "2021 / 4 / 1 0:00" represents the average value of "demand energy amount 7 days ago", "demand energy amount 14 days ago", and "demand energy amount 21 days ago" belonging to the row of the same time point.

[0048] After the rolling features are acquired, the rolling feature components are considered to be no longer necessary. Therefore, the preprocessing unit 12 may delete the rolling feature components from DZ3 after the rolling features are acquired. DZ4 in FIG. 10 shows an example of data generated by deleting the rolling feature components from DZ3.

[0049] Next, the preprocessing unit 12 derives a time equivalent, which is a feature corresponding to the time, by converting the time recorded in DZ4 into a predetermined format. Then, the preprocessing unit 12 adds the time equivalent to DZ4. As an example, the preprocessing unit 12 may set the time equivalent as an amount representing the elapsed time from the reference time "0:00" to the target time in hours (h). DZ5 in FIG. 11 shows an example of data generated by adding a time equivalent to DZ4.

[0050] In the example of Fig. 11, one minute of elapsed time from "0:00" corresponds to a time equivalent of 1 / 60 (≈0.01667). In the example of the first embodiment, the time equivalent is derived using only the hours (h) and minutes (m) without considering the year (y), month (m), and day (d) of the time. However, as will be apparent to those skilled in the art, an amount derived in a form that takes into account at least one of the year, month, and day may be derived as the time equivalent.

[0051] However, as will be apparent to those skilled in the art, the time equivalent does not necessarily have to be derived, because the prediction model according to one embodiment of the present disclosure only needs to be configured to output a predicted value of the demand energy amount by using a new feature amount derived by setting a special lag for the demand energy amount as an explanatory variable.

[0052] Next, the preprocessing unit 12 determines the day of the week corresponding to the date recorded in DZ5, for example by referring to calendar data. Then, the preprocessing unit 12 adds the day of the week to DZ5. DZ6 in FIG. 12 is an example of data generated by adding a day of the week to DZ5. In the example of FIG. 12, the preprocessing unit 12 generates DZ6 by adding the day of the week converted into a qualitative variable (nominal scale) to DZ5. In the example of the first embodiment, days of the week 0 to 6 represent Sunday to Saturday in that order. Therefore, day of the week 4 in the example of FIG. 12 represents Thursday.

[0053] 12, in DZ6, the demand energy amount and the new feature amount derived based on the demand energy amount may have a larger value than, for example, the feature amount indicating the day of the week and the working day (calendar feature amount). Therefore, the preprocessing unit 12 may perform scaling on each feature amount included in DZ6. This makes it possible to make the numerical range of each feature amount approximately the same, thereby making it possible to generate a prediction model with higher performance.

[0054] In the first embodiment, the pre-processing unit 12 performs standardization as scaling. The pre-processing unit 12 derives the average value (μ) and standard deviation (σ) of a certain feature included in DZ6. The pre-processing unit 12 then standardizes the feature using the derived μ and σ. In this specification, the μ and σ used for standardization before generating the learning model are referred to as standardization parameters. DZ6S in FIG. 13 is an example of data generated by standardizing each feature of DZ6. In the example of the first embodiment, DZ6 is used as pre-processed training data.

[0055] The learning unit 13 generates a prediction model by machine learning based on the time-series data of the demanded energy after the new feature amount derived based on the demanded energy is added. Therefore, as an example, the learning unit 13 may generate a prediction model based on the preprocessed training data (e.g., DZ6S) by executing a predetermined machine learning algorithm.

[0056] The prediction model according to an embodiment of the present disclosure may be a trained model capable of outputting a predicted value of the power demand at the time of prediction as a response variable. Therefore, the type of machine learning algorithm is not particularly limited as long as it is capable of solving a regression task. Examples of the machine learning algorithm include a neural network (NN), a support vector machine (SVM), and a decision tree (DT).

[0057] In an example of embodiment 1, the learning unit 13 uses (i) the demand energy amount at any prediction time included in DZ6S as the true value (correct data) of the objective variable, and (ii) each feature other than the demand energy amount prior to the prediction time as an explanatory variable, thereby generating a prediction model that derives the objective variable from the explanatory variables.

[0058] After generating the prediction model, the learning unit 13 may incorporate standardized parameters of each feature into the prediction model. The learning unit 13 may further incorporate a rolling lag and a non-rolling lag into the prediction model.

[0059] The evaluation unit 14 evaluates the prediction model generated by the learning unit 13. Specifically, the evaluation unit 14 evaluates the prediction performance of the prediction model. Prior to the evaluation by the evaluation unit 14, the preprocessing unit 12 generates preprocessed verification data by performing a series of preprocessing processes on the verification data in the same format as the training data described above. Therefore, in the example of the first embodiment, the preprocessed verification data has a data structure in the same format as DZ6S. Therefore, in the preprocessed verification data in the first embodiment, each feature is standardized using the standardization parameters incorporated in the prediction model.

[0060] The evaluation unit 14 evaluates the prediction model using the preprocessed verification data. Specifically, the evaluation unit 14 inputs the preprocessed verification data to the prediction model, causing the prediction model to output a predicted value. Then, the evaluation unit 14 derives an index value indicating the prediction accuracy of the prediction model based on the predicted value and the true value of the energy demand indicated in the preprocessed verification data.

[0061] The index value is not particularly limited as long as it is used in the machine learning field to evaluate the prediction accuracy in a regression task. Examples of the index value include Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The evaluation unit 14 may store the prediction model in the storage unit 90 after completing the derivation of the index value.

[0062] In addition, when the index value falls below a predetermined threshold, the model generation device 10 may cause the learning unit 13 to generate the prediction model again. In this case, prior to regeneration of the prediction model, for example, hyperparameters of the prediction model may be reset. In this way, in order to guarantee the performance of the prediction model used in the prediction phase, the generation of the prediction model may be repeated until an index value equal to or greater than the threshold is obtained. In this case, the evaluation unit 14 may store in the storage unit 90 the prediction model for which an index value equal to or greater than the threshold has been obtained for the first time.

[0063] (Example of a method for selecting elements of an arithmetic progression) 14 is a flowchart showing an example of a process flow for selecting arithmetic progression elements. First, the preprocessing unit 12 acquires the number of elements NPlag of the above-mentioned array Plag prior to S1. In the example of the first embodiment, Plag=[1, 7, 14, 21], so NPlag=4.

[0064] In S1, the preprocessing unit 12 determines whether NPlag is 2 or more. If NPlag is 2 or more (YES in S1), the process proceeds to S2. On the other hand, if NPlag is 1 or less (NO in S1), the preprocessing unit 12 ends the process in Fig. 14. In this case, the preprocessing unit 12 determines that Plag does not include an arithmetic progression element.

[0065] In S2, the preprocessing unit 12 initializes an element number i for indicating an element of Plag to 0. In S3, the preprocessing unit 12 initializes a flag flg indicating that Plag includes an arithmetic progression element to True.

[0066] In S4, the preprocessing unit 12 judges whether i is smaller than NPlag-2. If i is smaller than NPlag-2 (YES in S4), the process proceeds to S5. On the other hand, if i is equal to or larger than NPlag-2 (NO in S4), the process proceeds to S8.

[0067] In S5, the preprocessing unit 12 assigns Plag[i] to a0. a0 is the first term of the assumed arithmetic progression. Thus, in S5, the preprocessing unit 12 sets a0 to a value equal to Plag[i]. In S6, the preprocessing unit 12 assigns Plag[i+1]-a0 to d. d is the common difference of the assumed arithmetic progression. Thus, in S6, the preprocessing unit 12 sets d to a value equal to Plag[i+1]-a0. Next, in S7, the preprocessing unit 12 assigns i+2 to element number j for indicating an element of Plag. Then, the process proceeds to S11.

[0068] S8 to S10 are processes paired with S5 to S7, respectively. In S8, the pre-processing unit 12 assigns 0 to a0. That is, in S8, the pre-processing unit 12 sets a0 to 0. In S9, the pre-processing unit 12 assigns Plag[i]-a0 to d. Thus, in S9, the pre-processing unit 12 sets d to a value equal to Plag[i]-a0. Next, in S10, the pre-processing unit 12 assigns i+1 to j. Then, the process proceeds to S11.

[0069] In S11, the preprocessing unit 12 assigns Plag[j]-Plag[j-1] to next_d. Next_d is the next common difference of d in the assumed arithmetic progression. In this way, the preprocessing unit 12 sets next_d to a value equal to Plag[j]-Plag[j-1].

[0070] In S12, the preprocessing unit 12 determines whether next_d is equal to d. If next_d is equal to d (YES in S12), the preprocessing unit 12 counts up j by 1 in S13. Then, the process proceeds to S15. On the other hand, if next_d is not equal to d (NO in S12), the preprocessing unit 12 updates flg to False in S14. Then, the process proceeds to S16.

[0071] In S15, the preprocessing unit 12 judges whether or not j is equal to or greater than NPlag. If j is equal to or greater than NPlag (YES in S15), the process proceeds to S16. On the other hand, if j is smaller than NPlag (NO in S15), the process returns to S11. Thus, the processes of S11 to S13 and S15 are repeated until j becomes equal to NPlag.

[0072] In S16, the preprocessing unit 12 determines whether or not flg is False. If flg is False (YES in S16), in S17 the preprocessing unit 12 counts up i by 1. Then, the process proceeds to S19. On the other hand, if flg is not False (NO in S16), that is, if flg is True, the process proceeds to S18.

[0073] In S18, the preprocessing unit 12 assigns Plag[i:], which is the i-th and subsequent elements of Plag, to an array ap. The array ap is an array for storing arithmetic progression elements. After completing S18, the preprocessing unit 12 ends the processing of FIG. 14. If flg is True at the end of the processing (in other words, if the array ap is not an empty array at the end of the processing), the preprocessing unit 12 determines that Pla contains arithmetic progression elements. In this case, the preprocessing unit 12 may output ap as a selection result of arithmetic progression elements.

[0074] In S19, the preprocessing unit 12 judges whether i is equal to or greater than NPlag. If i is equal to or greater than NPlag (YES in S19), the process proceeds to S20. On the other hand, if i is smaller than NPlag (NO in S19), the process returns to S3. Thus, the processes of S3 to S16, S17, and S19 are repeated until i becomes equal to NPlag.

[0075] In S20, the preprocessing unit 12 assigns an empty array to the array ap. That is, the preprocessing unit 12 sets the array ap to an empty array. After completing S20, the preprocessing unit 12 ends the processing of FIG. 14. If flg is False at the end of the processing (in other words, if the array ap is an empty array at the end of the processing), the preprocessing unit 12 determines that Plag does not include an arithmetic progression element. In this case, the preprocessing unit 12 may output ap as an empty array as a selection result of an arithmetic progression element.

[0076] In the example of the first embodiment, after i is set to 0 in S2, a0=Plag[0]=1 is set in S5. Therefore, d=Plag[1]-a0=7-1=6 is set in S6. Then, j is set to 2 in S7. Therefore, next_d=Plag[2]-Plag[1]=14-7=7 is set in S11. In this case, since d is not equal to next_d, flg is set to False in S14. After that, i is counted up to 1 in S17.

[0077] After flg is updated to True in S3, a0=Plag[1]=7 is set in S5. Therefore, d=Plag[2]-a0=14-7=7 is set in S6. Then, j=3 is set in S7. Therefore, next_d=Plag[3]-Plag[2]=21-14=7 is set in S11. Therefore, since d is equal to next_d and flg is True, Plag[1:]=[7,14,21] is set as ap in S18.

[0078] (Prediction phase) Next, referring back to Fig. 1, an example of a prediction phase following the learning phase will be described. The prediction device 20 may include a data acquisition unit 21, a preprocessing unit 22, a prediction calculation unit 23, and a prediction result output unit 24. The data acquisition unit 21 may be referred to as a prediction-time data acquisition unit or a second data acquisition unit. The preprocessing unit 22 may be referred to as a prediction-time preprocessing unit or a second preprocessing unit.

[0079] As shown in FIG. 1, the memory unit 90 may store actual data DZP and workday data DSP used in the prediction phase. In the prediction phase, a demand energy amount prediction may be performed in real time. For this reason, the actual data DZP may be updated in real time. The data acquisition unit 21 acquires the DZP and the DSP as input data in the prediction phase. The DZP has a data structure in the same format as the DZ. The DSP has a data structure in the same format as the DS. In addition, the data acquisition unit 21 acquires the prediction model stored in the memory unit 90 by the model generation device 10 in the learning phase.

[0080] The pre-processing unit 22 performs a series of pre-processing steps on the input data set in the prediction phase in the same manner as in the learning phase described above, thereby generating pre-processed input data. An example of the processing performed by the pre-processing unit 22 is described below.

[0081] First, the pre-processing unit 22 performs pre-interpolation on the workday in the DSP to generate pre-interpolated workday data in the prediction phase (for convenience, referred to as DSP1). As a result of the pre-interpolation, DSP1 has the same time resolution (e.g., 1 minute) as DZP.

[0082] Next, the pre-processing unit 22 generates actual data with workday data in the prediction phase (for convenience, referred to as DZP1) by combining DZP and DSP1 using the time point as a key.

[0083] Next, the pre-processing unit 22 obtains the special lag determined in the learning phase. For example, the pre-processing unit 22 may read the rolling lag and the non-rolling lag determined in the learning phase from the prediction model. The pre-processing unit 22 derives new features in the prediction phase by setting the special lag determined in the learning phase to the power demand in the prediction phase.

[0084] The pre-processing unit 22 may derive a lag feature as a new feature in the prediction phase by setting a non-rolling lag for the demand energy in the prediction phase. Then, the pre-processing unit 22 may generate new data (for convenience, referred to as DZP2) by adding the lag feature to DZP1. In the example of the first embodiment, since the non-rolling lag is 1, the pre-processing unit 22 acquires the "demand energy one day ago" as the lag feature in the prediction phase.

[0085] Next, the preprocessing unit 22 may derive a rolling feature as a new feature in the prediction phase by setting a rolling lag for the demand power amount in the prediction phase. First, the preprocessing unit 22 acquires a rolling feature component in the prediction phase according to the rolling lag. In the example of the first embodiment, since the rolling lag is 7, 14, and 21, the preprocessing unit 22 acquires the "demand power amount 7 days ago", "demand power amount 14 days ago", and "demand power amount 21 days ago" in the prediction phase as the rolling feature components in the prediction phase. Then, the preprocessing unit 22 adds the rolling feature components to the DZP2.

[0086] Next, the preprocessing unit 22 derives an average value of the "energy demand 7 days ago", the "energy demand 14 days ago", and the "energy demand 21 days ago" in the prediction phase, and acquires the average value as a rolling feature in the prediction phase. Then, the preprocessing unit 22 further adds the rolling feature to the DZP2.

[0087] As described above, the preprocessing unit 22 may generate new data (for convenience, referred to as DZP3) by adding the rolling feature component and the rolling feature in the prediction phase to DZP2. After acquiring the rolling feature, the preprocessing unit 22 may generate new data (for convenience, referred to as DZP4) by deleting the rolling feature component from DZP3.

[0088] Next, the preprocessing unit 22 converts the time points recorded in the DZP4 into a time point equivalent amount in the prediction phase. Then, the preprocessing unit 22 generates new data (for convenience, referred to as DZP5) by adding the time point equivalent amount to the DZ4.

[0089] Next, the pre-processing unit 22 generates new data (for convenience, referred to as DZP6) by adding the day of the week (the day of the week converted into a qualitative variable) corresponding to the date recorded in DZP5 to DZ5.

[0090] Next, the preprocessing unit 22 reads out the standardized parameters determined in the learning phase from the prediction model. Then, the preprocessing unit 22 standardizes each feature of DZP6 using the standardized parameters. DZP6S in FIG. 15 is an example of data generated by standardizing each feature of DZP6. DZP6S is used as preprocessed input data in the prediction phase. In FIG. 15, the key time points are omitted. That is, in FIG. 15, only explanatory variables used to predict the demand energy in the prediction phase are shown.

[0091] As an example, in the forecast phase, a demand energy forecast may be performed every 30 minutes. In addition, in the forecast phase, a forecast value of the demand energy over a future period of, for example, more than 24 hours (e.g., from the present time to 36 hours later) may be derived. For this reason, in the forecast phase, the values ​​of some cells, for example, of the "demand energy one day ago" may be unknown. In the example of FIG. 15, a cell having an unknown value (i.e., a blank value) is indicated by the notation NULL. If a blank value exists in the DZP6S, the preprocessing unit 22 may interpolate the blank value using a predetermined interpolation method.

[0092] As an example, the preprocessing unit 22 may use the prediction model to interpolate a blank value based on a known value of the demand energy amount one day before. For example, the preprocessing unit 22 may input a known value of the demand energy amount one day before corresponding to a blank value into the prediction model, and cause the prediction model to output a predicted value according to the input. Then, the preprocessing unit 22 may set the predicted value as an interpolated value for the blank value.

[0093] The prediction calculation unit 23 causes the prediction model to output a predicted value of the amount of power demand based on the time-series data of the amount of power demand after a new feature value derived based on the amount of power demand in the prediction phase is added. Therefore, the prediction calculation unit 23 may cause the prediction model to output a predicted value by inputting preprocessed input data (e.g., DZP6S) to the prediction model. Specifically, the prediction calculation unit 23 supplies each feature value in the preprocessed input data to the prediction model as an explanatory variable. The prediction model outputs a predicted value according to the explanatory variable as a response variable.

[0094] The prediction result output unit 24 generates prediction result data based on the predicted value of the power demand output by the prediction model. For example, the prediction result output unit 24 may generate data in which each predicted value is associated with each time point as the prediction result data. The prediction result output unit 24 outputs the generated prediction result data.

[0095] Furthermore, the prediction result output unit 24 may convert the predicted value of the energy demand (unit: kWh) into another unit. As an example, the prediction result output unit 24 may convert the predicted value of the energy demand into a predicted value of the power demand (unit: kW). In this case, the prediction result output unit 24 may generate data in which each converted predicted value is associated with each time point as prediction result data.

[0096] (Effects of information processing device 1) According to the model generating device 10, in the learning phase, a special lag can be selected based on AC (autocorrelation coefficient of the power demand). Then, a new feature can be derived by setting the special lag for the power demand. In this way, according to the model generating device 10, the new feature can be derived based on AC, which is a feature expected to represent the periodicity of the fluctuation of the power demand (e.g., the above-mentioned rolling feature and lag feature). Therefore, it is possible to obtain the new feature as an explanatory variable expected to be useful for predicting the power demand.

[0097] The model generation device 10 can generate a prediction model based on time-series data to which new features are added to the power demand (for example, see DZ4 in FIG. 9 above). Therefore, a prediction model can be generated using the new features as explanatory variables, so that the prediction accuracy of the prediction model can be improved. In other words, a high-quality prediction model can be obtained.

[0098] The fluctuation trend of the power demand in a facility may differ depending on, for example, the type of business of the facility. For this reason, it is not necessarily easy for a user to manually set an appropriate lag according to the facility prior to generating a prediction model. Furthermore, if the lag set by the user is inappropriate, the quality of the prediction model may be degraded.

[0099] However, as described above, the model generation device 10 can select a special lag that is expected to be suitable for grasping the periodicity of fluctuations in the amount of power demand based on the AC. Therefore, a prediction model can be generated without the need for the user to manually set the lag. This makes it possible to obtain a high-quality prediction model suited to the type of business of the facility while improving user convenience.

[0100] Furthermore, the model generation device 10 can generate a prediction model using the employment status of the facility as an additional explanatory variable. By using the employment status as an additional explanatory variable, a prediction model that reflects the business condition of the facility in more detail can be generated. Therefore, it is possible to further improve the prediction accuracy of the prediction model.

[0101] Incidentally, Patent Document 1 shows the idea of ​​using multiple NNs in order to improve the accuracy of heat load prediction. Specifically, Patent Document 1 discloses that three separate NNs, an NN for weekdays, an NN for Saturdays, and an NN for Sundays, are generated in advance, and each NN is used depending on the day of the week of the prediction date.

[0102] However, depending on the type of business of the facility, at least one of Saturdays and Sundays may be set as working days. Alternatively, depending on the type of business of the facility, a specific weekday (e.g. Monday) may be set as a holiday. Alternatively, depending on the type of business of the facility, all of weekdays, Saturdays, and Sundays may be working days.

[0103] Therefore, the use of each NN shown in Patent Document 1 cannot flexibly deal with the various types of facilities described above. For this reason, when the use of each NN shown in Patent Document 1 is applied to power demand prediction, it is not necessarily possible to achieve high prediction accuracy.

[0104] On the other hand, as described above, the model generation device 10 can obtain a high-quality prediction model that is suited to the business condition of the facility. Therefore, unlike the technology of Patent Document 1, the model generation device 10 can generate a single prediction model that can achieve high prediction accuracy. In this way, the model generation device 10 can obtain a prediction model that is more versatile than the conventional ones.

[0105] Then, the prediction device 20 can derive a predicted value of the energy demand in the prediction phase by using the prediction model generated by the model generation device 10 in the learning phase. As described above, according to one aspect of the present invention, it is possible to improve the prediction accuracy of the energy demand in a facility compared to the conventional method.

[0106] [Embodiment 2] In the first embodiment, a method is exemplified in which a lag having a value equal to or greater than a time length threshold and corresponding to an AC having a value equal to or greater than an autocorrelation threshold is selected as a special lag from among a plurality of lags set in the learning phase. However, as will be apparent to those skilled in the art, the method of selecting a special lag is not limited to the example of the first embodiment.

[0107] For example, in the second embodiment, the preprocessing unit 12 may select, from among the multiple lags, a lag corresponding to a maximum value of AC in a data sequence indicating the relationship between the lag and AC as a special lag candidate. Thus, for example, the preprocessing unit 12 may select, as a special lag candidate, a lag corresponding to a maximum value of AC in the graph of Fig. 7. Next, the preprocessing unit 12 may select, from among the special lag candidates, a lag having a value equal to or greater than the time length threshold and corresponding to an AC having a value equal to or greater than the autocorrelation threshold as a special lag.

[0108] Depending on the type of business of the facility, there may be many lags corresponding to ACs having values ​​equal to or greater than the autocorrelation threshold value in the vicinity of the lag corresponding to the maximum value of AC. In such a case, if the selection method in the first embodiment is adopted, many special lags may be selected. In this case, many new explanatory variables (e.g., many rolling features and lag features) are derived. As is clear to those skilled in the art, if the number of explanatory variables is excessive, the quality of the prediction model may decrease. In addition, an excessive number of explanatory variables increases the time required to execute the machine learning algorithm.

[0109] On the other hand, in the above case, when the selection method in the second embodiment is adopted, a special lag is selected from among the special lag candidates, unlike the selection method in the first embodiment. Therefore, according to the selection method in the second embodiment, the number of selected special lags can be reduced compared to the selection method in the first embodiment.

[0110] Therefore, the selection method of the second embodiment can reduce the number of new explanatory variables derived, compared to the selection method of the first embodiment. As a result, the selection method of the second embodiment can generate a high-quality prediction model even in the above case. In addition, the time required to execute the machine learning algorithm can be reduced.

[0111] [Modifications] As will be apparent to those skilled in the art, the explanatory variables in the prediction model are not limited to the examples of the above embodiments. Any feature that is considered to affect the amount of power demand in a facility may be used as an explanatory variable. For example, depending on the type of business of the facility, meteorological conditions such as temperature, weather, and solar radiation affect the amount of power demand. Therefore, when there is a correlation between the meteorological conditions and the amount of power demand, a feature indicating the meteorological conditions (weather feature) can also be used as an additional explanatory variable.

[0112] The meteorological feature may be an actual value obtained as a result of measurement at a facility, or a predicted value provided by a weather forecast service provider. Thus, for example, in the learning phase, a prediction model may be generated using the actual value of the meteorological feature. Then, in the prediction phase, the predicted value of the meteorological feature may be used to predict the power demand by the prediction model. In this case, in the prediction phase, the predicted value of the meteorological feature may be corrected using the actual value of the meteorological feature in the learning phase.

[0113] [Software implementation example] The functions of the information processing device 1 (hereinafter referred to as the "device") can be realized by a program for causing a computer to function as the device, and a program for causing a computer to function as each control block of the device (particularly each part included in the control device 9).

[0114] In this case, the device includes a computer having at least one control device (e.g., a processor) and at least one storage device (e.g., a memory) as hardware for executing the program. The control device and storage device execute the program to realize each function described in each of the above embodiments.

[0115] The program may be non-transitory and may be recorded in one or more computer-readable recording media. The recording media may or may not be included in the device. In the latter case, the program may be provided to the device via any wired or wireless transmission medium.

[0116] In addition, some or all of the functions of each of the control blocks can be realized by a logic circuit. For example, an integrated circuit in which a logic circuit that functions as each of the control blocks is formed is also included in the scope of one aspect of the present invention. In addition, the functions of each of the control blocks can be realized by, for example, a quantum computer.

[0117] As is clear from the description of each of the above embodiments, each process in each of the above embodiments can be executed by AI (Artificial Intelligence). In this case, the AI ​​may be operated by the control device or may be operated by another device (for example, an edge computer or a cloud server).

[0118] 〔summary〕 A model generation device according to a first aspect of the present invention is a model generation device that generates a prediction model for predicting the amount of electricity demand in a facility, wherein the model generation device sets multiple lags for time series data showing the time progression of actual values ​​of the amount of electricity demand, derives autocorrelation coefficients of the actual values ​​corresponding to each of the multiple lags, selects a special lag from among the multiple lags based on the autocorrelation coefficients, derives a new feature by setting the special lag for the actual value, and generates the prediction model by machine learning based on the time series data to which the new feature has been added.

[0119] A model generating device according to aspect 2 of the present invention may, in aspect 1, select as the special lag a lag from among the plurality of lags, the lag having a value equal to or greater than a time length threshold and corresponding to the autocorrelation coefficient having a value equal to or greater than an autocorrelation threshold.

[0120] In the model generating device of aspect 3 of the present invention, in aspect 1, a lag corresponding to a maximum value of the autocorrelation coefficient in a data series indicating the relationship between the lag and the autocorrelation coefficient may be selected as a special lag candidate from among the multiple lags, and a lag having a value equal to or greater than a time length threshold and corresponding to the autocorrelation coefficient having a value equal to or greater than an autocorrelation threshold may be selected as a special lag from among the special lag candidates.

[0121] In the model generating device according to a fourth aspect of the present invention, in the second or third aspect, the autocorrelation threshold may be a value not less than 0.7 and not more than 1.

[0122] A model generation device according to aspect 5 of the present invention, in any one of aspects 2 to 4 above, may select, as rolling lags, a plurality of special lags forming an arithmetic progression in an array in which the plurality of special lags are sorted in ascending order, and may derive a rolling feature as the new feature by setting the rolling lag to the actual value.

[0123] A model generating device according to aspect 6 of the present invention may, in aspect 5, select the special lags in the array excluding the rolling lag as non-rolling lags, and may derive a lag feature as the new feature by setting the non-rolling lag to the actual value.

[0124] In a model generating device according to aspect 7 of the present invention, in any one of aspects 1 to 6 above, the time series data to which the new feature has been added may further include data indicating daily working conditions in the facility.

[0125] A prediction device according to an eighth aspect of the present invention is a prediction device that derives a predicted value of the demand energy by using a prediction model for predicting the demand energy in a facility, which was generated in advance in a learning phase, in a prediction phase. In the learning phase, for time series data in the learning phase showing a time progression of an actual value of the demand energy, a plurality of lags are set for the actual value, thereby deriving an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags, a special lag is selected from among the plurality of lags based on the autocorrelation coefficient, a new feature is derived by setting the special lag for the actual value, and the prediction model is generated by machine learning based on the time series data to which the new feature has been added, and the prediction device derives a new feature for the prediction phase by setting the special lag selected in the learning phase for the time series data in the prediction phase showing a time progression of the actual value, and outputs the predicted value to the prediction model based on the time series data in the prediction phase to which the new feature has been added.

[0126] A model generation method according to a ninth aspect of the present invention is a model generation method for generating a predictive model for predicting the amount of electricity demand in a facility, and includes the steps of: setting multiple lags for time series data showing the time progression of actual values ​​of the amount of electricity demand in the facility, thereby deriving an autocorrelation coefficient of the actual values ​​for each of the multiple lags; selecting a special lag from among the multiple lags based on the autocorrelation coefficients; deriving a new feature by setting the special lag for the actual value; and generating the predictive model by machine learning based on the time series data to which the new feature has been added.

[0127] A prediction method according to a tenth aspect of the present invention is a prediction method for deriving a predicted value of an amount of energy demand in a facility by using, in a prediction phase, a prediction model for predicting an amount of energy demand in the facility, which was generated in advance in a learning phase. In the learning phase, for time-series data in the learning phase showing a time transition of an actual value of the amount of energy demand in the facility, a plurality of lags are set for the actual value, and an autocorrelation coefficient of the actual value is derived for each of the plurality of lags. A special lag is selected from among the plurality of lags based on the autocorrelation coefficient, and a new value is derived by setting the special lag for the actual value in the learning phase. The prediction model is generated by machine learning based on the time series data in the learning phase to which a feature has been derived and the new feature has been added, and the prediction method includes: a step of deriving a new feature for the prediction phase by setting the special lag selected in the learning phase to the actual value in the prediction phase for the time series data in the prediction phase indicating a time progression of an actual value of electricity demand in the facility; and a step of outputting the predicted value to the prediction model based on the time series data in the prediction phase to which the new feature has been added.

[0128] [Additional Notes] One aspect of the present invention is not limited to the above-described embodiments, and various modifications are possible within the scope of the claims. Embodiments obtained by appropriately combining the technical means disclosed in different embodiments are also included in the technical scope of one aspect of the present invention. [Explanation of symbols]

[0129] 1. Information processing device 10 Model generation device 11 Data acquisition unit (data acquisition unit of model generating device) 12 Pre-processing section (pre-processing section of model generating device) 13 Learning Department 14 Evaluation Section 20 Prediction Device 21 Data acquisition unit (data acquisition unit of prediction device) 22 Preprocessing section (preprocessing section of prediction device) 23 Prediction calculation section 24 Prediction result output section

Claims

1. A model generation device that generates a prediction model for predicting an amount of power demand in a facility, The model generating device comprises: With respect to time series data indicating a time transition of an actual value of the energy demand, a plurality of lags are set for the actual value, and an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags is derived; selecting a particular lag from among the plurality of lags based on the autocorrelation coefficient; A new feature quantity is derived by setting the special lag to the actual value, and a model generation device that generates the prediction model by machine learning based on the time-series data to which the new feature has been added.

2. 2. The model generating device of claim 1, wherein the model generating device selects, from among the plurality of lags, a lag having a value equal to or greater than a time length threshold and corresponding to the autocorrelation coefficient having a value equal to or greater than an autocorrelation threshold as the special lag.

3. The model generating device comprises: Among the plurality of lags, a lag corresponding to a maximum value of the autocorrelation coefficient in a data sequence indicating a relationship between the lags and the autocorrelation coefficient is selected as a special lag candidate; The model generating device according to claim 1 , wherein, from among the special lag candidates, a lag having a value equal to or greater than a time length threshold and corresponding to the autocorrelation coefficient having a value equal to or greater than an autocorrelation threshold is selected as the special lag.

4. 3. The model generating apparatus of claim 2, wherein the autocorrelation threshold is a value greater than or equal to 0.7 and less than or equal to 1.

5. The model generating device comprises: selecting a plurality of the special lugs in an arithmetic progression as rolling lugs from an array in which the plurality of special lugs are sorted in ascending order; 3. The model generating device according to claim 2, wherein the rolling lag is set to the actual value, thereby deriving a rolling feature as the new feature.

6. The model generating device comprises: selecting the special lugs in the arrangement other than the rolling lug as non-rolling lugs; The model generating device according to claim 5 , wherein the non-rolling lag is set for the actual value to thereby derive a lag feature as the new feature.

7. 2. The model generating device according to claim 1, wherein the time series data to which the new feature has been added further includes data indicating daily working conditions in the facility.

8. A prediction device that derives a predicted value of an amount of power demand in a facility by using a prediction model for predicting an amount of power demand in a facility, the prediction model having been generated in advance in a learning phase, the prediction device comprising: In the learning phase, a plurality of lags are set for the time-series data in the learning phase indicating a time transition of the actual value of the energy demand, and an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags is derived; a special lag is selected from the plurality of lags based on the autocorrelation coefficient; A new feature is derived by setting the special lag to the actual value, The prediction model is generated by machine learning based on the time series data to which the new feature has been added; The prediction device includes: deriving new features in the prediction phase by setting the special lag to the actual values ​​in the prediction phase for time-series data in the prediction phase that indicates a time transition of the actual values; a prediction device that causes the prediction model to output the predicted value based on the time series data in the prediction phase to which the new feature amount in the prediction phase has been added.

9. A model generation method for generating a prediction model for predicting an amount of power demand in a facility, comprising: A step of setting a plurality of lags for time-series data indicating a time transition of an actual value of an amount of energy demand in the facility, and deriving an autocorrelation coefficient of the actual value for each of the plurality of lags; selecting a particular lag from among the plurality of lags based on the autocorrelation coefficient; deriving a new feature quantity by setting the specific lag to the actual value; generating the prediction model by machine learning based on the time series data to which the new feature has been added.

10. A prediction method for deriving a predicted value of an amount of power demand in a facility by using a prediction model for predicting an amount of power demand in a facility, the prediction model having been generated in advance in a learning phase, the prediction method comprising: In the learning phase, a plurality of lags are set for the time-series data in the learning phase indicating a time transition of the actual value of the energy demand, and an autocorrelation coefficient of the actual value corresponding to each of the plurality of lags is derived; a special lag is selected from the plurality of lags based on the autocorrelation coefficient; A new feature is derived by setting the special lag to the actual value, The prediction model is generated by machine learning based on the time series data to which the new feature has been added; The above prediction method is deriving new feature quantities in the prediction phase by setting the specific lag to the actual values ​​in the prediction phase for time-series data in the prediction phase that indicates a time transition of the actual values; and causing the prediction model to output the predicted value based on the time series data in the prediction phase to which the new feature in the prediction phase has been added.

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