BEHAVIOR PREDICTION DEVICE, BEHAVIOR PREDICTION METHOD, AND BEHAVIOR PREDICTION PROGRAM

Abstract

To provide a behavior prediction device, a behavior prediction method, and a behavior prediction program, capable of improving prediction accuracy for behaviors of vehicles participating in VGI.SOLUTION: A behavior prediction device calculates a model predicted value (prediction value) obtained by statistically predicting future behavior of a vehicle based on past behavior history of the vehicle, and acquires a user input value (scheduled value) representing scheduled future behavior of the vehicle from a user. A first integrated error which is obtained by integrating an error between the model predicted value and an actual value of the vehicle behavior during a predetermined period, and a second integrated error which is obtained by integrating an error between the user input value and the actual value during the same period are calculated. The behavior prediction device determines that the user input value is a final predicted value of the future behavior when the first integrated error is smaller than the second integrated error, and determines that the model predicted value is the final predicted value when the first integrated error is equal to or larger than the second integrated error.SELECTED DRAWING: Figure 5

Description

[Technical field]

[0001] The present disclosure relates to a behavior prediction device, a behavior prediction method, and a behavior prediction program for predicting the behavior of vehicles participating in VGI. [Background technology]

[0002] Nowadays, research on Vehicle-Grid Integration (VGI), which uses electric vehicles (including pure electric vehicles and plug-in hybrid vehicles that use batteries as their only energy source) as part of the power supply infrastructure, is progressing. One example is disclosed in Patent Document 1. Patent Document 1 discloses a method of storing past operational performance data of a vehicle together with attribute data of the acquisition date, including date, day of the week, and weather, and estimating an operational plan for the vehicle from the performance data corresponding to attribute data similar to or similar to that of the prediction target date. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2016-134160 A Summary of the Invention [Problem to be solved by the invention]

[0004] However, the operation plan of each vehicle is not necessarily similar to the past operation record. Therefore, a prediction model based only on the past operation record data cannot predict the behavior of the vehicle with high accuracy. In particular, in the case of vehicles that do not have a fixed behavior pattern, a discrepancy is likely to occur between the prediction by the prediction model and the reality.

[0005] An object of the present disclosure is to provide a behavior prediction device, a behavior prediction method, and a behavior prediction program that can improve the prediction accuracy of the behavior of vehicles participating in VGI. [Means for solving the problem]

[0006] A behavior prediction device according to the present disclosure includes at least one memory storing at least one program, and at least one processor coupled to the at least one memory. The at least one processor executes the at least one program to perform the following first to sixth processes.

[0007] The first process is a process of calculating a predicted value that statistically predicts the future behavior of the vehicle based on the past behavior history of the vehicle. The second process is a process of acquiring a scheduled value that indicates a schedule of the future behavior of the vehicle from a user who uses the vehicle. The third process is a process of calculating the reliability of a predicted value based on a behavior history based on an error in a past predetermined period of time of a predicted value based on a behavior history with respect to an actual value of the behavior of the vehicle. The fourth process is a process of calculating the reliability of a scheduled value acquired from a user based on an error in a past predetermined period of time of a scheduled value acquired from a user with respect to an actual value of the behavior of the vehicle. The fifth process is a process that is executed when the reliability of the scheduled value acquired from the user is higher than the reliability of the predicted value based on the behavior history, and the scheduled value acquired from the user is determined as a final predicted value of the future behavior of the vehicle. The sixth process is a process that is executed when the reliability of the scheduled value acquired from the user is equal to or lower than the reliability of the predicted value based on the behavior history, and the predicted value based on the behavior history is determined as a final predicted value.

[0008] In the behavior prediction device, when the scheduled values ​​acquired from the user include a one-off scheduled value representing a one-off schedule that is not a recurring schedule, the at least one processor may determine the one-off scheduled value as the final predicted value in priority to the predicted value based on the behavior history. When the scheduled values ​​acquired from the user include a plurality of one-off scheduled values, the latest one-off scheduled value may be determined as the final predicted value. In addition, as a numerical value correlating with the reliability of the predicted value based on the behavior history, an integrated value of an error with respect to the actual value of the predicted value based on the behavior history may be calculated, and as a numerical value correlating with the reliability of the scheduled value acquired from the user, an integrated value of an error with respect to the actual value of the scheduled value acquired from the user may be calculated.

[0009] The behavior prediction method according to the present disclosure includes the following first to sixth steps. The first step is a step of calculating a predicted value that statistically predicts the future behavior of the vehicle based on the past behavior history of the vehicle. The second step is a step of acquiring a scheduled value that indicates a schedule of the future behavior of the vehicle from a user who uses the vehicle. The third step is a step of calculating the reliability of the predicted value based on the behavior history based on an error in a past predetermined period of time of the predicted value based on the behavior history with respect to the actual value of the behavior of the vehicle. The fourth step is a step of calculating the reliability of the scheduled value acquired from the user based on an error in a past predetermined period of time of the scheduled value acquired from the user with respect to the actual value of the behavior of the vehicle. The fifth step is a step that is executed when the reliability of the scheduled value acquired from the user is higher than the reliability of the predicted value based on the behavior history, and is a step of determining the scheduled value acquired from the user as a final predicted value of the future behavior of the vehicle. The sixth step is a step that is executed when the reliability of the scheduled value acquired from the user is equal to or lower than the reliability of the predicted value based on the behavior history, and is a step of determining the predicted value based on the behavior history as a final predicted value.

[0010] The behavior prediction program according to the present disclosure is a program that causes a computer to predict the behavior of vehicles participating in a VGI, and causes the computer to execute the first to sixth processes described above. Effect of the Invention

[0011] According to the behavior prediction device, behavior prediction method, and behavior prediction program of the present disclosure, not only is a statistically predicted value calculated based on past behavior history, but also a scheduled value indicating the vehicle's future planned behavior is acquired from the user. Then, of the predicted value based on the behavior history and the scheduled value acquired from the user, the one with higher reliability in light of the past behavior track record is determined as the final predicted value. This can improve the prediction accuracy of the behavior of vehicles participating in VGI, and can also handle vehicles that do not have a fixed behavior pattern.

[0012] In addition, when the scheduled values ​​obtained from the user include a one-off scheduled value, the one-off scheduled value is determined as the final predicted value in priority to the predicted value based on the behavioral history, which makes it possible to respond to sudden schedules for the vehicle. [Brief description of the drawings]

[0013] [Figure 1] 1 is a diagram illustrating an overall configuration of a VGI to which a behavior prediction device according to an embodiment of the present disclosure is applied. [Diagram 2] 1 is a block diagram showing a configuration of a VGI control system including a behavior prediction device according to an embodiment of the present disclosure. [Diagram 3] 2 is a block diagram showing a configuration of a vehicle behavior prediction unit as a behavior prediction device according to an embodiment of the present disclosure. FIG. [Figure 4] 10 is a diagram for explaining an overview of a process performed by a vehicle behavior prediction unit; FIG. [Diagram 5] 1 is a flowchart illustrating a behavior prediction method according to an embodiment of the present disclosure. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0014] Hereinafter, the embodiments of the present disclosure will be described with reference to the drawings. However, when the number, quantity, amount, range, etc. of each element is mentioned in the embodiments shown below, the idea of ​​the present disclosure is not limited to the mentioned number unless it is specifically stated or clearly specified in principle. Furthermore, the structures etc. described in the embodiments shown below are not necessarily essential to the idea of ​​the present disclosure unless it is specifically stated or clearly specified in principle.

[0015] 1. Overall structure of VGI Fig. 1 is a diagram showing the overall configuration of a VGI 2 according to an embodiment of the present disclosure. In the VGI 2, electricity is traded between an energy trading market 4, an aggregator 6, and a VGI control system 8. Although Fig. 1 shows a one-to-one relationship between the energy trading market 4 and the aggregator 6, multiple aggregators 6 may be related to one energy trading market 4. Furthermore, Fig. 1 shows a one-to-one relationship between the aggregator 6 and the VGI control system 8, but multiple VGI control systems 8 may be related to one aggregator 6.

[0016] The energy trading market 4 calls for an energy adjuster to balance the supply and demand of electricity to the aggregator 6. The energy trading market 4 provides the aggregator 6 with information such as the desired electricity purchase and sale price and the required procurement time, along with the call for an energy adjuster. The aggregator 6 decides whether to bid or respond to the call based on the information provided by the energy trading market 4. When the aggregator 6 bids or responds to the call from the energy trading market 4 and an electricity transaction is concluded between the energy trading market 4 and the aggregator 6, the aggregator 6 adjusts the electricity by charging and discharging using the VGI control system 8. The aggregator 6 reports the actual amount of charging and discharging achieved by the VGI control system 8 to the energy trading market 4, and receives a reward from the energy trading market 4.

[0017] The aggregator 6 receives a report from the VGI control system 8 on the amount of electricity that the VGI control system 8 can trade with the aggregator 6 (hereinafter referred to as the amount of electricity tradable) by the end of the day. Then, based on the amount of electricity tradable provided, the amount of charging and discharging electricity and the time of charging and discharging requested from the VGI control system 8 are calculated, and a power amount adjustment plan is presented to the VGI control system 8 by the day before or by the morning of the day when power adjustment is required. The VGI control system 8 tallies the amount of electricity charged to the vehicles 14 participating in VGI2 and the amount of electricity discharged from the vehicles 14, reports the results to the aggregator 6, and receives a reward from the aggregator 6. The vehicles 14 participating in VGI2 are electric vehicles including pure electric vehicles (EVs) and plug-in hybrid vehicles (PHVs). An EV is a vehicle that runs on an electric motor using only a battery 16 as an energy source. A PHV is a vehicle that has an electric motor and an internal combustion engine, and can directly charge the battery 16, which is the energy source of the electric motor, from the outside.

[0018] The VGI control system 8 instructs each vehicle 14 to charge or discharge based on the power adjustment plan received from the aggregator 6, and transmits control data to the charging and discharging stations 12 under the management of the VGI control system 8. The charging and discharging instructions are given by mobile communications such as 4G and 5G. The control data is transmitted via a communication network including the Internet. The vehicle 14 instructed to charge or discharge is connected to the charging and discharging station 12, and charging and discharging are performed between the charging and discharging station 12 and the vehicle 14 in accordance with the control data, thereby adjusting the charging and discharging power of each vehicle 14.

[0019] Furthermore, in the VGI2, a data server 10 is used. The data server 10 manages data on all vehicles 14 participating in the VGI2. The data managed by the data server 10 includes a vehicle ID for identifying the vehicle 14, the current location of each vehicle 14, the charge state and degradation state of the battery 16 of each vehicle 14, and the past behavior history of each vehicle 14. The behavior history of the vehicle 14 includes the history of departure time, history of return time, history of parking location, history of travel distance (or history of travel energy), and the like. The data server 10 individually collects vehicle data from each vehicle 14 by mobile communication, and updates the managed data of each vehicle 14 to the latest information.

[0020] The data managed by the data server 10 is used in the VGI control system 8 to predict the behavior of each vehicle 14. The prediction of the behavior of the vehicle 14 in the VGI control system 8 will be described in detail in the next chapter and subsequent chapters.

[0021] 2.VGI Control System Configuration The configuration of the VGI control system 8 will be described with reference to Fig. 2. Fig. 2 is a block diagram showing the configuration of the VGI control system 8. The VGI control system 8 includes, as its physical configuration, at least one processor 8a (hereinafter simply referred to as a processor) and at least one memory 8b (hereinafter simply referred to as a memory) coupled to the processor 8a. The memory 8b stores at least one program 8c (hereinafter simply referred to as a program) executable by the processor 8a and various data related thereto. The processor 8a executes the program 8c to realize various processes by the processor 8a.

[0022] The VGI control system 8 includes a vehicle behavior prediction unit 81, an electricity tradeable amount prediction unit 82, a charge / discharge plan creation unit 83, a charge / discharge instruction unit 84, and a performance aggregation unit 85. These are realized as functions of the VGI control system 8 when a program 8c stored in the memory 8b is executed by the processor 8a. In particular, the vehicle behavior prediction unit 81 is a function of the VGI control system 8 as the behavior prediction device of this embodiment.

[0023] The vehicle behavior prediction unit 81 individually predicts the behavior of all vehicles 14 participating in VGI2, for example, the behavior of the vehicles 14 tomorrow. The behavior of the vehicles 14 predicted by the vehicle behavior prediction unit 81 includes the departure time, return time, parking location, and travel distance (or travel energy), etc. Prediction data acquired from the data server 10 is used for the behavior prediction of the vehicles 14 by the vehicle behavior prediction unit 81. The function of the vehicle behavior prediction unit 81 will be described in detail later. The vehicle behavior prediction unit 81 provides the behavior prediction data obtained by the behavior prediction for each vehicle 14 to the power tradeable amount prediction unit 82 and the charge / discharge plan creation unit 83.

[0024] The tradable electricity amount prediction unit 82 predicts the tradable electricity amount based on the behavior prediction data for each vehicle 14 provided from the vehicle behavior prediction unit 81. The state of charge of the battery 16 of the vehicle 14 depends on the behavior of the vehicle 14, and the opportunity for charging and discharging between the charging and discharging station 12 and the vehicle 14 also depends on the behavior of the vehicle 14. Therefore, if the behavior of each vehicle 14 tomorrow can be predicted, the amount of charging and discharging between each vehicle 14 and the VGI control system 8 can be predicted. Then, the tradable electricity amount can be predicted by aggregating the predicted values ​​of the charging and discharging amounts for all the vehicles 14. The tradable electricity amount prediction unit 82 calculates the predicted value of the charging and discharging amount for each vehicle 14 from the behavior prediction data for each vehicle 14, and calculates the tradable electricity amount by aggregating the predicted values ​​of the charging and discharging amount for each vehicle 14.

[0025] The charge / discharge plan creation unit 83 creates a charge / discharge plan for each vehicle 14 to satisfy the power adjustment plan, based on the power amount adjustment plan acquired from the aggregator 6 and the behavior prediction data for each vehicle 14 provided from the vehicle behavior prediction unit 81. The charge / discharge plan creation unit 83 creates a charge / discharge plan that is optimized to simultaneously maximize the electricity profit and minimize the deterioration of the battery 16, for example, within a range that does not impair the original purpose of the vehicle, which is related to the movement of the vehicle 14. The charge / discharge plan creation unit 83 provides the created charge / discharge plan to the charge / discharge instruction unit 84.

[0026] The charge / discharge instruction unit 84 instructs each vehicle 14 to charge / discharge according to the charge / discharge plan provided from the charge / discharge plan creation unit 83. The charge / discharge instruction from the charge / discharge instruction unit 84 to the vehicle 14 may be given to a car navigation system or an information terminal carried by the user of the vehicle 14. Alternatively, if it is determined by contract, for example, that charging / discharging is to be performed as instructed, the charge / discharge instruction may be given to an ECU (Electronic Control Unit) that controls the vehicle 14. Although not shown in the figure, the charge / discharge instruction unit 84 transmits control data for each vehicle 14 created according to the charge / discharge plan to the charging / discharging station 12. When the vehicle 14 is connected to the charging / discharging station 12 and the charging / discharging station 12 operates according to the control data, charging / discharging according to the charge / discharge plan is performed in the vehicle 14.

[0027] The result tallying unit 85 collects the charging and discharging results for each vehicle 14 from each vehicle 14. The charging and discharging results may be collected directly from each vehicle 14 via mobile communication with the vehicle 14, or may be collected from the charging and discharging station 12 that performed charging and discharging with the vehicle 14. The result tallying unit 85 tallys up the charging and discharging results collected from each vehicle 14, and calculates the charging and discharging results of the VGI control system 8 as a whole. Then, the result tallying unit 85 reports the charging and discharging results of the VGI control system 8 as a whole to the aggregator 6.

[0028] As explained above, the behavior prediction data for each vehicle 14 obtained by the vehicle behavior prediction unit 81 is used in the calculation of the amount of tradable electricity in the VGI control system 8, and is also used to plan a charge / discharge plan for each vehicle 14. Therefore, if it is possible to predict with high accuracy how each vehicle 14 will behave, it is possible to predict with high accuracy the amount of tradable electricity, and it is possible to plan a highly feasible charge / discharge plan. As a result, the amount of electricity that can be traded in the energy trading market 4 increases, and charging / discharging can be reliably performed according to the advance charge / discharge plan for each vehicle 14. In other words, by improving the accuracy of the behavior prediction of the vehicles 14, it is possible to increase the profitability of the VGI 2.

[0029] 3. Configuration of vehicle behavior prediction unit The configuration of the vehicle behavior prediction unit 81 will be described with reference to Fig. 3. Fig. 3 is a block diagram showing the configuration of the vehicle behavior prediction unit 81. In the following description, the behavior of the vehicle 14 predicted by the vehicle behavior prediction unit 81 is assumed to be the departure time, return time, parking location, and mileage. However, instead of the mileage, electric energy obtained by multiplying the mileage (km) by the estimated power consumption (kWh / km), i.e., the electric energy to be used for the next trip, may be used.

[0030] The vehicle behavior prediction unit 81 includes a departure time prediction unit 811, a return time prediction unit 812, a parking location prediction unit 813, and a mileage prediction unit 814. These are realized as functions of the VGI control system 8 as the vehicle behavior prediction unit 81 when the program 8c stored in the memory 8b is executed by the processor 8a.

[0031] The departure time prediction unit 811, the return time prediction unit 812, the parking location prediction unit 813, and the mileage prediction unit 814 each include a prediction model 811a, 812a, 813a, and 814a. The prediction models 811a, 812a, 813a, and 814a are statistical models for statistically predicting the future behavior of the vehicle 14 from the past behavior history, and are created using, for example, Bayesian estimation or machine learning. In detail, the prediction model 811a of the departure time prediction unit 811 is created based on past history data of the departure time, and is successively learned and updated based on the most recent history data. The prediction model 812a of the return time prediction unit 812 is created based on past history data of the return time, and is successively learned and updated based on the most recent history data. The prediction model 813a of the parking location prediction unit 813 is created based on past history data of the parking location, and is successively learned and updated based on the most recent history data. The prediction model 814a of the mileage prediction unit 814 is created based on past history data of mileage, and is successively updated through learning based on the most recent history data.

[0032] The behavior history data and the user input data are input from the data server 10 to each of the departure time prediction unit 811, the return time prediction unit 812, the parking location prediction unit 813, and the mileage prediction unit 814. More specifically, the departure time prediction unit 811 receives the departure time history data and the user input data. The departure time prediction unit 811 calculates the predicted departure time based on those data. The return time prediction unit 812 receives the return time history data and the user input data. The return time prediction unit 812 calculates the predicted return time based on those data. The parking location prediction unit 813 receives the parking location history data and the user input data. The parking location prediction unit 813 calculates the predicted parking location based on those data. And the mileage prediction unit 814 receives the mileage history data and the user input data. The mileage prediction unit 814 calculates the predicted mileage based on those data. In this way, the vehicle behavior prediction unit 81 calculates the predicted departure time, predicted return time, predicted parking location, and predicted travel distance independently of each other.

[0033] As described above, the vehicle behavior prediction unit 81 uses not only the behavior history data of the vehicle 14 but also the user input data to predict the behavior of the vehicle 14. The user input data is data related to the schedule of the behavior of the vehicle 14 input by the user who uses the vehicle 14. However, although the user input data is registered in the data server 10 together with the behavior route history data in FIG. 3, the user input data may be registered in a server different from the data server 10. Below, an overview of the processing by the vehicle behavior prediction unit 81 will be described with reference to FIG. 4.

[0034] The most recent behavior history data is input to the vehicle behavior prediction unit 81. The behavior history data includes, as data items, departure time history, return time history, parking time history, and mileage history. Each data item is expressed as a numerical value. The most recent behavior history data input is used to learn and update the prediction model. When learning and updating the prediction model, attributes of the data acquisition date on which the behavior history data was obtained are associated with the behavior history data. The attributes of the data acquisition date here are attributes that affect the behavior of the vehicle 14, such as the day of the week, public holiday, consecutive holidays, weather, season, etc.

[0035] The vehicle behavior prediction unit 81 inputs attributes of a prediction target date for which the behavior of the vehicle 20 is to be predicted into a prediction model, and obtains model prediction data. For example, if the prediction target date is Monday, the weather forecast is sunny, and the season is summer, such information is input into the prediction model as attributes of the prediction target date. The model prediction data obtained by the prediction model includes the following data items: predicted departure time, predicted return time, predicted parking time, and predicted mileage. Each data item is expressed as a numerical value.

[0036] User input data is input to the vehicle behavior prediction unit 81. The user input data is data related to the planned behavior of the vehicle 14, which is acquired from user information registered on a smartphone or a website, such as the user's personal schedule. The planned behavior of the vehicle 14 includes a repeated schedule (fixed schedule) that is repeated, for example, on a weekly or monthly basis, and a one-off schedule that is not repeated. The user input data includes, as data items, a planned departure time, a planned return time, a planned parking time, and a planned mileage. Each data item is expressed by a numerical value. However, data does not need to be input for all of these data items. It is entirely optional for the user to input these data, and only when the user inputs data, the input data is used to predict the behavior history of the vehicle 14.

[0037] The vehicle behavior prediction unit 81 selects either the model prediction data or the user input data as the final prediction data according to the result of the condition judgment. The final prediction data includes the predicted departure time, the predicted return time, the predicted parking time, and the predicted mileage as data items. Each data item is expressed by a numerical value. The condition judgment is performed from the viewpoint of the reliability of the data, as will be described later in the flowchart. That is, if it is determined that the user input data is more reliable than the model prediction data, the user input data is determined as the final prediction data. Conversely, if it is determined that the model prediction data is more reliable than the user input data, the model prediction data is determined as the final prediction data. However, since the reliability differs for each data item, the selection of the final prediction data is performed for each data item. For example, there may be a case where the user input data is used only for the predicted departure time, and the model prediction data is used for the other data items.

[0038] 4. Method for predicting vehicle behavior In this embodiment, the behavior of the vehicle 14 is predicted by the vehicle behavior prediction unit 81 having the above configuration. Hereinafter, the behavior prediction method by the vehicle behavior prediction unit 81 will be described with reference to Fig. 5. Fig. 5 shows a flowchart of the procedure of the behavior prediction method by the vehicle behavior prediction unit 81. The vehicle behavior prediction unit 81 executes the procedure shown in this flowchart for each data item.

[0039] 5, in step S1, the most recent behavioral history data is acquired from the data server 10. In step S2, prediction data is learned and updated using the behavioral history data acquired in step S1.

[0040] Next, in step S3, attributes of the prediction target day are acquired. If the attributes include weather or temperature, information is acquired from the weather forecast for the prediction target day. In step S4, the attributes of the prediction target day acquired in step S3 are input into the prediction model updated in step S2, thereby calculating a predicted value of the behavior of the vehicle 20 on the prediction target day. The predicted value calculated in step S4 is the value of the data item to be predicted in the model prediction data shown in FIG. 4.

[0041] Next, in step S5, the data server 10 is referenced and it is determined whether or not user input data is registered in the data server 10 for the data item to be predicted on the prediction target date. If there is no user input data for the data item to be predicted on the prediction target date, that is, if the user has not input data, step S12 is executed. In step S12, the predicted value by the prediction model calculated in step S4 is determined as the final predicted value. The final predicted value calculated in step S12 is the value of the data item to be predicted in the final prediction data shown in FIG. 4.

[0042] If the result of the determination in step S5 is that user input data exists in the data server 10 for the data item to be predicted on the prediction target date, then the determination in step S6 is performed. In step S6, it is determined whether the user input data whose registration was confirmed in step S5 includes a one-time schedule. If the user input data includes a one-time schedule, step S11 is executed. In step S11, the final predicted value is determined by giving priority to the one-time schedule. Giving priority to the one-time schedule means that when a recurring schedule such as a weekly schedule or a monthly schedule is also registered on the prediction target date, the one-time schedule, not the recurring schedule, is determined as the final predicted value. Furthermore, when multiple one-time schedules are registered in the same data item on the prediction target date, the latest one-time schedule is determined as the final predicted value.

[0043] If the determination result of step S6 is that there is no one-time schedule and only a recurring schedule such as a weekly schedule or a monthly schedule, steps S7 and S8 are executed. In step S7, a first integrated error is calculated by integrating the error of the predicted value with respect to the actual value within a predetermined past period. The predicted value here is a numerical value representing the behavior of the vehicle 14 statistically predicted based on the past behavior history, and is the value of the data item to be predicted in the user input data shown in FIG. 4. The calculation formula for the first integrated error can be expressed by the following formula 1. In formula 1, N is the number of data in the predetermined past period. TIFF0007600804000001.tif13150

[0044] In step S8, a second integrated error is calculated by integrating the error of the planned value against the actual value within a predetermined period of time in the past. The planned value here is a numerical value representing the planned behavior of the vehicle 14 input by the user, and is the value of the data item to be predicted in the user input data shown in Fig. 4. The calculation formula for the second integrated error can be expressed by the following formula 2. N in formula 2 is the number of data in the predetermined period of time in the past. TIFF0007600804000002.tif13150

[0045] Then, in step S9, the first integrated error calculated in step S7 is compared with the second integrated error calculated in step S8. The smaller the error of the predicted value with respect to the actual value, the higher the reliability of the predicted value. Therefore, the first integrated error can be regarded as a numerical value correlating with the reliability of the predicted value. Also, the smaller the error of the planned value with respect to the actual value, the higher the reliability of the planned value. Therefore, by comparing the first integrated error with the second integrated error, it is possible to determine which is more reliable: the predicted value statistically predicted based on the past behavior history, or the planned value input by the user.

[0046] If the result of the determination in step S9 is that the first integrated error is greater than the second integrated error, step S10 is executed. In step S10, the scheduled value input by the user is determined as the final predicted value. On the other hand, if the first integrated error is equal to or less than the second integrated error, step S12 is executed. In step S12, the predicted value according to the prediction model calculated in step S4, i.e., the predicted value statistically predicted based on the past behavior history, is determined as the final predicted value.

[0047] As mentioned above, it is not necessary for the user to input data for all data items. For this reason, for example, the scheduled departure time may be input but the scheduled return time may not be input. In that case, the scheduled departure time input by the user is selected as the final predicted value of the departure time, but the predicted return time by the prediction model may be selected as the final predicted value of the return time. In such a case, it cannot be said that there is no possibility that the final predicted value of the departure time will be later than the final predicted value of the return time. Regarding this problem, the vehicle behavior prediction unit 81 uses the previous value without adopting the current value when a contradiction occurs between the final predicted value of the departure time and the final predicted value of the return time.

[0048] 5. Effects of this embodiment A prediction model that makes statistical predictions using the behavior history data of the vehicle 14 allows for highly accurate predictions of stationary behavior patterns, such as weekday commuting patterns, for example. However, on the other hand, it is difficult for the prediction model to handle vehicles that do not have fixed behavior patterns or unexpected schedules.

[0049] In this regard, in this embodiment, not only is a predicted value of the behavior of the vehicle 14 calculated by the prediction model, but also a scheduled value representing the schedule of the future behavior of the vehicle 14 is input by the user. Then, of the predicted value by the prediction model and the scheduled value input by the user, the one with higher reliability in light of the past behavior track record is determined as the final predicted value. This can improve the prediction accuracy of the behavior of the vehicle 14 participating in VGI2, and can also handle vehicles that do not have a fixed behavior pattern. Furthermore, if the scheduled value input by the user includes a one-time scheduled value, the one-time scheduled value is determined as the final predicted value in priority to the predicted value by the prediction model, so that it is possible to handle sudden schedules of the vehicle 14. [Explanation of symbols]

[0050] 2. VGI 4. Electricity trading market 6. Aggregators 8. VGI Control System 8a Processor 8b Memory 8c Program 10 Data Server 12 Charging and discharging stand 14 Vehicles 16 Battery 81 Vehicle Behavior Prediction Department 82 Electricity Trading Availability Forecasting Department 83 Charging and Discharging Planning Department 84 Charge / discharge instruction section 85 Performance Compilation Department

Claims

1. A behavior prediction device for predicting a behavior of a vehicle participating in a VGI, comprising: at least one memory storing at least one program; at least one processor coupled to the at least one memory; The at least one memory stores a model for statistically predicting a future behavior of the vehicle based on a past behavior history of the vehicle, the predictive model being created using Bayesian estimation or machine learning; The behavior history includes a departure time history, a return time history, a parking location history, and a mileage history, The at least one processor, by executing the at least one program, learning and updating the prediction model using the most recent behavioral history data including a departure time history, a return time history, a parking time history, and a mileage history as data items, and attributes of the data acquisition date on which the behavioral history data was obtained; The attribute includes at least one of a day of the week, a public holiday, a consecutive holiday, weather, and a season; inputting the attributes of the vehicle on a prediction date for predicting the vehicle's behavior into the prediction model, thereby obtaining model prediction data from the prediction model, the model prediction data including a predicted departure time, a predicted return time, a predicted parking time, and a predicted mileage as data items; Acquire user input data from a user who uses the vehicle, the data items including a planned departure time, a planned return time, a planned parking time, and a planned driving distance of the vehicle; calculating a first integrated error for each data item by integrating an error for each data item between past model prediction data obtained by the prediction model during a past predetermined period and actual data of the behavior of the vehicle corresponding to the past model prediction data for the past predetermined period; calculating a second integrated error for each data item by integrating an error for each data item between past user input data input by the user during the past specified period and the performance data of the vehicle action corresponding to the past user input data for each data item for the past specified period; For a data item in which the first integrated error is greater than the second integrated error, the user input data is determined as final predicted data of the future behavior of the vehicle; For a data item whose first integrated error is equal to or smaller than the second integrated error, the model predicted data is determined to be the final predicted data. A behavior prediction device comprising:

2. The behavior prediction device according to claim 1 , The at least one processor, by executing the at least one program, When any of the data items of the user input data acquired from the user includes a one-off schedule that is not a recurring schedule, the user input data is determined as the final prediction data for the data item including the one-off schedule in preference to the model prediction data. A behavior prediction device comprising:

3. A behavior prediction method for predicting behavior of a vehicle participating in a VGI using a computer, comprising: A step of storing in advance in a memory a model for statistically predicting a future behavior of the vehicle based on a past behavior history of the vehicle, the model being created using Bayesian estimation or machine learning; The behavior history includes a departure time history, a return time history, a parking location history, and a mileage history, A step of learning and updating the prediction model using the most recent behavioral history data including a departure time history, a return time history, a parking time history, and a mileage history as data items and attributes of the data acquisition date on which the behavioral history data was obtained; The attribute includes at least one of a day of the week, a public holiday, a consecutive holiday, weather, and a season; acquiring model prediction data from the prediction model, the model prediction data including a predicted departure time, a predicted return time, a predicted parking time, and a predicted mileage as data items, by inputting the attributes of the prediction date for predicting the vehicle behavior into the prediction model; A step of acquiring user input data including a planned departure time, a planned return time, a planned parking time, and a planned driving distance of the vehicle from a user who uses the vehicle; calculating a first integrated error for each data item by integrating an error for each data item between past model prediction data obtained by the prediction model during a past predetermined period of time and actual data of the behavior of the vehicle corresponding to the past model prediction data for the past predetermined period of time for the data item; calculating a second integrated error for each data item by integrating an error for each data item between past user input data input by the user during the past predetermined period and actual data of the behavior of the vehicle corresponding to the past user input data for the past predetermined period for each data item; For a data item in which the first integrated error is greater than the second integrated error, determining the user input data as final prediction data of the future behavior of the vehicle; For a data item whose first integrated error is equal to or smaller than the second integrated error, determining the model predicted data as the final predicted data. A behavior prediction method comprising:

4. A behavior prediction program that causes a computer to predict the behavior of vehicles participating in a VGI, reading from a memory a model for statistically predicting a future behavior of the vehicle based on a past behavior history of the vehicle, the prediction model being created using Bayesian estimation or machine learning; The behavior history includes a departure time history, a return time history, a parking location history, and a mileage history, A previous prediction model is learned and updated using the most recent behavioral history data including a departure time history, a return time history, a parking time history, and a mileage history as data items and attributes of the data acquisition date on which the behavioral history data was obtained; The attribute includes at least one of a day of the week, a public holiday, a consecutive holiday, weather, and a season; inputting the attributes of the vehicle on a prediction date for predicting the vehicle's behavior into the prediction model, thereby obtaining model prediction data from the prediction model, the model prediction data including a predicted departure time, a predicted return time, a predicted parking time, and a predicted mileage as data items; Acquire user input data from a user who uses the vehicle, the data items including a planned departure time, a planned return time, a planned parking time, and a planned driving distance of the vehicle; calculating a first integrated error for each data item by integrating an error for each data item between past model prediction data obtained by the prediction model during a past predetermined period and actual data of the behavior of the vehicle corresponding to the past model prediction data for the past predetermined period; calculating a second integrated error for each data item by integrating an error for each data item between past user input data input by the user during the past specified period and the performance data of the vehicle action corresponding to the past user input data for each data item for the past specified period; For a data item in which the first integrated error is greater than the second integrated error, the user input data is determined as final predicted data of the future behavior of the vehicle; determining, for a data item in which the first integrated error is equal to or smaller than the second integrated error, the model predicted data as the final predicted data. A behavior prediction program characterized by:

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