Charging pile ordered charging load prediction method and system, electronic equipment and storage medium

Abstract

The invention provides a charging pile ordered charging load prediction method and system, electronic equipment and a storage medium, and belongs to the technical field of electric traffic and power load prediction.The method comprises the steps that electric vehicle operation data at different time points in a to-be-predicted area are obtained, and an electric vehicle operation database is constructed; constructing an electric vehicle user portrait classification model according to the electric vehicle operation database to form a user portrait set; according to the user portrait set in combination with the positions of the charging piles, predicting the charging behavior possibility and the charging amount of each charging pile at different time points; selecting a prediction date, judging the type of the prediction date, and performing classified fitting on a charging pile load curve; and matching the charging pile load curve with the possibility of the charging behavior of each charging pile at different time points and the charging amount, fitting the charging pile load curve into a charging pile load prediction curve of each charging pile, and performing prediction. According to the method, data dimension expansion is realized, prediction deviation is avoided, the accuracy is high, and the application range is wide.

Description

Background Art

[0002] With the popularity of electric vehicles, the number of charging piles is also increasing rapidly. The installation and use of a large number of charging piles has brought challenges to the safety and stability of the power grid. In order to improve power efficiency and reduce the overall power load, power supply units have the need to optimize resource allocation and balance the supply and demand of electricity. The formulation of relevant plans needs to rely on load estimation in the power consumption area. Therefore, accurate short-term power load forecasting of charging piles and appropriate evaluation of charging pile power consumption levels are crucial to building a safe and stable power grid.

[0003] At present, the prediction methods in the field of power load can be generally divided into long-term load prediction, medium-term load prediction and short-term load prediction according to the prediction time span. The time span of short-term load prediction is generally within one month, which is mainly used for real-time safety analysis and scheduling of power systems to meet power generation needs and ensure safe and stable operation of power systems. In the prior art, the short-term load prediction method of charging piles is usually based on the statistical analysis of the historical charging data of the charging piles, such as collecting the charging times, charging time and charging power of the charging piles within a period of time, and then fitting through some common mathematical models, such as linear regression models, to obtain the short-term charging pile load prediction results.

[0004] However, in actual operation, the charging situation of charging piles is affected by many external factors, and its short-term load changes are unstable. The data dimension of the existing short-term load prediction method for charging piles is small, and it is unable to accurately reflect the actual charging needs of electric vehicle users, resulting in low short-term prediction accuracy; at the same time, the existing short-term load prediction method for charging piles does not fully consider the differences in travel and charging patterns of electric vehicle users on weekdays and holidays, which further leads to low prediction accuracy and large deviations; in addition, the existing short-term load prediction method for charging piles can usually only obtain relatively rough prediction results, and it is impossible to carry out refined charging arrangements and load management guidance for a single charging pile, and it is also difficult to realize multi-dimensional data analysis of different types of charging piles in regions, cities, etc., and its application is relatively limited. Summary of the invention

[0005] In view of the technical problems that the existing short-term load forecasting methods for charging piles have small data dimensions, do not take into account the differences between weekdays and holidays, and have low forecast accuracy; can only obtain relatively rough forecast results and have limited applications, the invention provides a method, system, electronic device and storage medium for forecasting the orderly charging load of charging piles, which combine the operation data of electric vehicles and the charging data of charging piles to achieve the expansion of data dimensions and improve the accuracy of charging pile load forecasting; classify weekdays and holidays to avoid forecasting deviations and further improve accuracy; can provide refined guidance on the charging arrangement and load management of a single charging pile, and can also conduct multi-dimensional data analysis at the regional and city levels for each type of charging pile, with a wide range of applications.

[0006] In a first aspect, the present invention provides a method for predicting the orderly charging load of a charging pile, the steps comprising: S1. Obtaining electric vehicle operation data at different time points in the prediction area, and using the electric vehicle operation data to build an electric vehicle operation database; S2. Build an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and form a user portrait for each electric vehicle, and all user portraits form a user portrait set; S3. Predict the time and location of electric vehicle charging behavior based on the user portrait set, and predict the possibility of charging behavior and the required charging amount of each charging pile at different time points based on the location of each charging pile in the predicted area; S4. Select a forecast date and determine whether the forecast date is a working day or a holiday; when the forecast date is a working day, obtain the charging data of the charging piles in the forecast area on working days within a period of time, perform regression calculation on the charging data on working days, and fit a charging pile load curve that feedbacks the load of each charging pile over time within a day; When the prediction date is a holiday, the holiday charging data of the charging piles in the prediction area within a period of time is obtained, and regression calculation is performed on the holiday charging data to fit a charging pile load curve that reflects the load change of each charging pile over time within a day; S5. Match the charging pile load curve with the possibility of charging behavior and the required charging amount of each charging pile at different time points in step S3, and finally fit it into a charging pile load prediction curve for each charging pile, and predict the load of each charging pile at different time points on the prediction date.

[0007] It should be further explained that, in step S1, the electric vehicle operation data includes static operation data and dynamic data, the static data includes vehicle identification, vehicle purpose and / or vehicle energy consumption, and the dynamic operation data includes the vehicle's average daily mileage, the vehicle's average daily trip times, the vehicle's battery capacity, the vehicle's average charge per trip, the vehicle's charging status parameters, the vehicle's charging time point and / or the vehicle's charging location.

[0008] It should be further explained that, in step S2, the user portrait of the electric vehicle includes vehicle identification information, driving information and charging information; The vehicle identification information includes the vehicle number, vehicle model, vehicle VIN code, vehicle license plate number and / or vehicle purpose; Driving information includes monthly vehicle travel days, monthly vehicle travel mileage, and average vehicle travel mileage; The charging information includes the monthly charging amount of the vehicle, the monthly charging times of the vehicle, the average charging amount per vehicle, the distribution of vehicle charging time periods and / or the distribution of vehicle charging locations.

[0009] It should be further explained that the user profile of an electric vehicle also includes vehicle usage flexibility information, and the vehicle usage flexibility information includes vehicle time flexibility, vehicle space flexibility, vehicle energy flexibility and / or vehicle management flexibility.

[0010] It should be further explained that, in step S1, each day is divided into 96 time points, with a difference of 15 minutes between two adjacent time points, and the electric vehicle operation data at each time point in the area to be predicted is obtained; In step S4, a day is divided into 96 time points, with a difference of 15 minutes between two adjacent time points. When the predicted date is a weekday, the charging data of the charging piles in the test area at each time point in the 10 working days before the predicted date is obtained to form the weekday charging data; when the predicted date is a holiday, the charging data of the charging piles in the test area at each time point in the 10 holidays before the predicted date is obtained to form the holiday charging data.

[0011] It should be further noted that the method further includes step S6: summarizing the charging load prediction curves of all charging piles in the area to be predicted and fitting them into a total charging pile load prediction curve; The charging pile load prediction curves of the charging piles are divided according to the regions where the charging piles are located and the types of the charging piles. The charging pile load prediction curves of each region and each type are summarized and fitted to form regional charging pile load prediction curves for each region, classified charging pile load prediction curves for each type of charging pile and regional classified charging pile load prediction curves for each type of charging pile in each region.

[0012] It should be further explained that the types of charging piles include public charging piles, dedicated charging piles and private charging piles, among which dedicated charging piles include bus-dedicated charging piles, logistics park-dedicated charging piles and / or airport-dedicated charging piles.

[0013] In a second aspect, the present invention provides a charging pile orderly charging load prediction system, which is used to implement the above-mentioned charging pile orderly charging load prediction method, comprising: A data acquisition module is used to acquire the electric vehicle operation data at different time points in the area to be predicted; A user portrait building module is used to build an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and form a user portrait for each electric vehicle, and finally form a user portrait set; The charging behavior prediction module is used to predict the time and location of electric vehicle charging behavior based on the user portrait set, and further predict the possibility of charging behavior and the required charging amount of each charging pile at different time points in combination with the location of each charging pile in the predicted area; The load curve fitting module is used to obtain the corresponding charging data of the charging pile according to whether the selected prediction date is a working day or a holiday, and perform regression calculation to fit the load curve of each charging pile; The prediction curve synthesis module is used to match the fitted charging pile load curve with the previously predicted possibility of charging behavior and the required charging amount of each charging pile at different time points, and finally fit it into the load prediction curve of each charging pile to realize the load prediction of each charging pile at different time points on the prediction date; The result display module is used to display the load forecast curve and corresponding data in the form of graphics and data.

[0014] In a third aspect, the present invention provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor is configured to implement the steps of the above-mentioned method for predicting the orderly charging load of a charging pile when executing the computer program.

[0015] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of the above-mentioned method for predicting the orderly charging load of a charging pile are implemented.

[0016] The beneficial effects of the present invention are: 1. The method for predicting the orderly charging load of charging piles of the present invention combines the electric vehicle operation data and the charging data of the charging piles in the area to be predicted, integrates the underlying data of the supply side and the demand side, and finally fits the charging pile load prediction curve of each charging pile, predicts the load of each charging pile at different time points on the prediction date, realizes the expansion of data dimensions, and improves the accuracy of charging pile load prediction.

[0017] 2. The present invention performs classification processing after selecting a prediction date and determining whether the prediction date is a working day or a holiday, which fully considers the different travel and charging modes of users on working days and holidays, effectively avoids prediction deviations caused by unified processing, and further improves the accuracy of prediction results.

[0018] 3. The charging pile load prediction curve of a single charging pile obtained by the charging pile orderly charging load prediction method of the present invention can predict the load of the charging pile at different time points, which is helpful to provide refined guidance on the charging arrangement and load management of a single charging pile; and the load prediction curve of a single charging pile is divided and summarized according to the region and the type of charging pile, so that each type of charging pile can be subjected to multi-dimensional data analysis at the regional level and the city level, and then the load prediction technology can be automatically realized through the systematic application technology, and the output load prediction result can be used as the basis for the formulation of the precise control plan of the vehicle-grid interactive load. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order to more clearly illustrate the technical solution of the present invention, the accompanying drawings required for use in the description will be briefly introduced below. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0020] Figure 1 It is a flow chart of a method for predicting the orderly charging load of a charging pile in one embodiment of the present invention.

[0021] Figure 2 It is a schematic block diagram of a charging pile orderly charging load prediction system in one embodiment of the present invention.

[0022] Figure 3 It is a schematic diagram of the hardware structure of an electronic device in one embodiment of the present invention. DETAILED DESCRIPTION

[0023] In order to enable those skilled in the art to better understand the technical solutions in the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present invention.

[0024] The method for predicting the orderly charging load of charging piles involved in the present application is mainly aimed at the technical field of electric transportation and power load prediction. The beneficial effect is that the underlying data of the supply side and the demand side are integrated by combining the electric vehicle operation data and the charging data of the charging piles in the area to be predicted, and finally fitted into a charging pile load prediction curve for each charging pile, and the load of each charging pile at different time points on the prediction date is predicted, thereby realizing the expansion of data dimensions and improving the accuracy of charging pile load prediction; after the selected prediction date is determined to be the type of the prediction date as a weekday or a holiday, classification processing is performed, which fully considers the different travel and charging modes of users on weekdays and holidays, and effectively avoids the difference due to uniformity. The prediction deviation caused by the processing further improves the accuracy of the prediction result; the charging pile load prediction curve of a single charging pile obtained by the charging pile orderly charging load prediction method of the present invention can predict the load of the charging pile at different time points, which is helpful to provide refined guidance on the charging arrangement and load management of a single charging pile; and the load prediction curve of a single charging pile is divided and summarized according to the region and the type of charging pile, so that each type of charging pile can be subjected to multi-dimensional data analysis at the regional level and the city level, and then the load prediction technology can be automatically realized through the systematic application of technology, and the output load prediction result can be used as the basis for the formulation of a precise control plan for the interactive load of the vehicle network.

[0025] The method for predicting the orderly charging load of charging piles involved in this application is mainly aimed at the technical problems that the existing short-term load prediction methods of charging piles have small data dimensions, do not take into account the differences between weekdays and holidays, and have low prediction accuracy; only relatively rough prediction results can be obtained, and the application is limited.

[0026] The following is a detailed description of the method for predicting the orderly charging load of a charging pile involved in the present application. For the purpose of illustration rather than limitation, specific details such as specific system structures and technologies are proposed to facilitate a thorough understanding of the embodiments of the present application. However, it should be clear to those skilled in the art that the present application can also be implemented in other embodiments without these specific details.

[0027] In the method for predicting the orderly charging load of charging piles involved in this application, the term "including" used indicates the existence of the described features, entities, steps, operations, elements and / or components, but does not exclude the existence or addition of one or more other features, entities, steps, operations, elements, components and / or their collections. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized.

[0028] In order to clearly describe the technical solution of the present application, the words "first", "second" and the like are used to distinguish the same or similar items with substantially the same functions and effects. Those skilled in the art can understand that the words "first", "second" and the like do not limit the quantity and execution order, and the words "first", "second" and the like do not necessarily limit the difference.

[0029] The phrases such as "one embodiment" or "some embodiments" described in the present application mean that the specific features, structures or characteristics described in the embodiment are included in one or more embodiments of the present application. Therefore, the phrases such as "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments" etc. that appear in different places in the present application do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways.

[0030] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0031] The method for predicting the orderly charging load of a charging pile provided in the embodiment of the present invention is executed by a computer device, and accordingly, the system for predicting the orderly charging load of a charging pile runs in the computer device.

[0032] Figure 1 : is a flow chart of a method for predicting the orderly charging load of a charging pile according to an embodiment of the present invention. Figure 1 The execution subject may be a charging pile orderly charging load prediction system. According to different requirements, the order of the steps in the flow chart may be changed, and some may be omitted.

[0033] like Figure 1 As shown, the charging pile orderly charging load prediction method includes: Step S1. Obtaining electric vehicle operation data at different time points in the area to be predicted, and using the electric vehicle operation data to construct an electric vehicle operation database.

[0034] Collecting electric vehicle operation data at different time points and building an electric vehicle operation database can provide a comprehensive understanding of the use of electric vehicles, provide rich materials for in-depth mining of user behavior patterns and charging needs, and enable prediction models to be built based on actual vehicle usage.

[0035] In some embodiments, the electric vehicle operation data includes static operation data and dynamic data, the static data includes vehicle identification, vehicle purpose and / or vehicle energy consumption, and the dynamic operation data includes the vehicle's average daily mileage, the vehicle's average daily trips, the vehicle's battery capacity, the vehicle's average charge per trip, the vehicle's charging status parameters, the vehicle's charging time and / or the vehicle's charging location.

[0036] The above electric vehicle operation data can comprehensively reflect the usage characteristics of electric vehicles. Static data can help understand the basic properties and performance of electric vehicles, and dynamic data can help to gain an in-depth understanding of the actual operation and charging conditions of electric vehicles, providing sufficient information basis for the subsequent construction of accurate user portraits and prediction models.

[0037] In some embodiments, each day is divided into 96 time points, with a 15-minute difference between two adjacent time points, and the electric vehicle operation data at each time point in the area to be predicted is obtained.

[0038] Dividing each day into 96 time points and obtaining the electric vehicle operation data at each time point in the test area can carefully capture the changing patterns of the electric vehicle operation data. When collecting the electric vehicle operation data and fitting the load curve, it can accurately reflect the characteristics of different time periods, thereby improving the time accuracy of the prediction.

[0039] Step S2, constructing an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and forming a user portrait for each electric vehicle, and all user portraits form a user portrait set.

[0040] By building a user portrait classification model, each electric vehicle can be classified and characterized. The resulting user portrait set can help grasp the characteristics of different user groups, such as driving habits, charging frequency and location, etc. This helps to make personalized charging behavior predictions for different types of users and improve the accuracy and pertinence of the predictions.

[0041] In some embodiments, the user profile of the electric vehicle includes vehicle identification information, driving information, and charging information; The vehicle identification information includes the vehicle number, vehicle model, vehicle VIN code, vehicle license plate number and / or vehicle purpose; Driving information includes monthly vehicle travel days, monthly vehicle travel mileage, and average vehicle travel mileage; The charging information includes the monthly charging amount of the vehicle, the monthly charging times of the vehicle, the average charging amount per vehicle, the distribution of vehicle charging time periods and / or the distribution of vehicle charging locations.

[0042] In some embodiments, the user profile of the electric vehicle also includes vehicle usage flexibility information, and the vehicle usage flexibility information includes vehicle time flexibility, vehicle space flexibility, vehicle energy flexibility and / or vehicle management flexibility.

[0043] Acquiring the above vehicle identification information can make the user portrait more rich and specific, and improve the accuracy of charging behavior prediction based on the user portrait.

[0044] Step S3, predicting the time and location of the electric vehicle charging behavior based on the user portrait set, and predicting the possibility of charging behavior and the required charging amount of each charging pile at different time points in combination with the location of each charging pile in the predicted area.

[0045] Predicting the possibility of charging behavior at each charging pile at different times and the required charging amount can enable operators to understand the potential usage of each charging pile in advance and improve the accuracy of charging pile load prediction.

[0046] Step S4, selecting a forecast date, and determining whether the forecast date is a working day or a holiday; when the forecast date is a working day, obtaining the charging data of the charging piles in the forecast area on working days within a period of time, performing regression calculation on the charging data on working days, and fitting a charging pile load curve that feeds back the load change of each charging pile over time within a day; When the prediction date is a holiday, the holiday charging data of the charging piles in the prediction area within a period of time is obtained, and regression calculation is performed on the holiday charging data to fit a charging pile load curve that reflects the load changes of each charging pile over time within a day.

[0047] Due to the differences in user travel patterns and activity ranges on weekdays and holidays, the charging needs of electric vehicles are also different. Differentiating between weekdays and holidays for charging data acquisition and load curve fitting takes into account the differences in user charging behaviors on these two types of dates, which can more accurately reflect the changing patterns of actual charging needs. The fitted load curve can intuitively show the load change trend of the charging pile within a day.

[0048] In some embodiments, a day is divided into 96 time points, with a difference of 15 minutes between two adjacent time points. When the predicted date is a weekday, the charging data of the charging piles in the test area at each time point in the 10 working days before the predicted date is obtained to form the weekday charging data; when the predicted date is a holiday, the charging data of the charging piles in the test area at each time point in the 10 holidays before the predicted date is obtained to form the holiday charging data.

[0049] Each day is divided into 96 time points, and the charging data of the charging piles in the test area at each time point in the 10 working days before the prediction date is obtained to form weekday charging data or holiday charging data. This can carefully capture the changing patterns of the charging conditions of the charging piles, and when fitting the load curve, it can accurately reflect the characteristics of different time periods, thereby improving the time accuracy of the prediction.

[0050] Step S5, matching the charging pile load curve with the possibility of charging behavior and the required charging amount of each charging pile at different time points in step S3, and finally fitting it into a charging pile load prediction curve for each charging pile, and predicting the load of each charging pile at different time points on the prediction date.

[0051] By synthesizing the load forecast curve, we can accurately predict the load of each charging pile at different time points. The prediction results can be directly applied to the operation and management of the charging piles, such as making preparations for power supply in advance and reasonably arranging user charging time, which helps to achieve orderly charging and improve the efficiency of charging piles.

[0052] In some embodiments, the method further includes step S6: summarizing the charging load prediction curves of all charging piles in the area to be predicted and fitting them into a total charging pile load prediction curve; The charging pile load prediction curves of the charging piles are divided according to the regions where the charging piles are located and the types of the charging piles. The charging pile load prediction curves of each region and each type are summarized and fitted to form regional charging pile load prediction curves for each region, classified charging pile load prediction curves for each type of charging pile and regional classified charging pile load prediction curves for each type of charging pile in each region.

[0053] Summarizing the charging load forecast curves of all charging piles and fitting the total load forecast curve, and dividing and fitting them by region and charging pile type, will help to grasp the charging demand situation in the entire predicted area and the load characteristics of different regions and different types of charging piles from a macro perspective. It is of great value for power grid dispatching, regional charging facility planning, and operation and management of different types of charging piles, and can achieve more comprehensive resource optimization allocation.

[0054] In some embodiments, the types of charging piles include public charging piles, dedicated charging piles and private charging piles, wherein the dedicated charging piles include bus-dedicated charging piles, logistics park-dedicated charging piles and / or airport-dedicated charging piles.

[0055] Different types of charging piles serve different user groups, whose charging needs and usage patterns vary greatly. Classifying the types of charging piles helps to conduct more targeted load forecasting and operation management based on the characteristics and usage scenarios of different types of charging piles.

[0056] In a specific embodiment, the method for predicting the orderly charging load of a charging pile includes: Step S1. Divide each day into 96 time points, with a 15-minute difference between two adjacent time points, obtain the electric vehicle operation data at each time point in the prediction area, and use the electric vehicle operation data to build an electric vehicle operation database; Electric vehicle operation data includes static operation data and dynamic data. Static data includes vehicle identification, vehicle purpose and vehicle energy consumption. Dynamic operation data includes vehicle average daily mileage, vehicle average daily trip times, vehicle battery capacity, vehicle average charge per trip, vehicle charging status parameters, vehicle charging time and vehicle charging location. Step S2. constructing an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and forming a user portrait for each electric vehicle, and all user portraits form a user portrait set; The user profile of electric vehicles includes vehicle identification information, driving information, and charging information; Among them, vehicle identification information includes vehicle number, vehicle model, vehicle VIN code, vehicle license plate number, vehicle purpose and vehicle use flexibility information; Driving information includes monthly vehicle travel days, monthly vehicle travel mileage, and average vehicle travel mileage; Charging information includes monthly vehicle charging volume, monthly vehicle charging times, average vehicle charging volume per time, distribution of vehicle charging time periods, and / or distribution of vehicle charging locations; The vehicle use flexibility information includes vehicle time flexibility, vehicle space flexibility, vehicle energy flexibility and / or vehicle management flexibility; Step S3. Predicting the time and location of electric vehicle charging behavior based on the user portrait set, and predicting the possibility of charging behavior and the required charging amount of each charging pile at different time points in combination with the location of each charging pile in the predicted area; Step S4. Select a forecast date and determine whether the forecast date is a working day or a holiday; divide a day into 96 time points, with a difference of 15 minutes between two adjacent time points; When the forecast date is a working day, the charging data of the charging piles in the test area at each time point in the 10 working days before the forecast date is obtained to form the working day charging data. The working day charging data is regressed and fitted to obtain a charging pile load curve that reflects the load change of each charging pile over time within a day. When the forecast date is a holiday, the charging data of the charging piles in the test area at each time point in the 10 holidays before the forecast date is obtained to form holiday charging data. Regression calculation is performed on the holiday charging data to fit the charging pile load curve that feedbacks the load change of each charging pile over time within a day; Step S5. Match the charging pile load curve with the possibility of charging behavior and the required charging amount of each charging pile at different time points in step S3, and finally fit it into a charging pile load prediction curve for each charging pile, and predict the load of each charging pile at different time points on the prediction date; Step S6. Summarize the charging load prediction curves of all charging piles in the prediction area and fit them into a total charging pile load prediction curve; The charging pile load prediction curves of the charging piles are divided according to the regions where the charging piles are located and the types of the charging piles, and the charging pile load prediction curves of each region and each type are summarized and fitted to form regional charging pile load prediction curves for each region, classified charging pile load prediction curves for each type of charging pile, and regional classified charging pile load prediction curves for each type of charging pile in each region; Among them, the types of charging piles include public charging piles, dedicated charging piles and private charging piles, among which dedicated charging piles include bus-dedicated charging piles, logistics park-dedicated charging piles and / or airport-dedicated charging piles.

[0057] The above method is used to control charging piles at all bus stations in Beijing. The prediction date is October 11, 2023, and the load prediction curve of bus-specific charging piles is fitted. According to the load prediction curve of bus-specific charging piles, the predicted peak of bus daily charging load on October 11, 2023 will appear at 9:00-10:00 and 19:00-20:00, with a peak load of about 150,000 kilowatts. According to the load forecast data, the overall load will be reduced by 20% from 10:00 to 12:00 in the morning and from 18:00 to 20:00 at night on October 11, and finally the maximum load reduction in the city will be 25,000 kilowatts.

[0058] The following is an embodiment of the active load shedding optimization control system for a wind farm provided by the embodiments of the present disclosure. The active load shedding optimization system and the orderly charging load prediction method for charging piles in the above-mentioned embodiments belong to the same inventive concept. For details not fully described in the embodiment of the active load shedding optimization control system for a wind farm, reference can be made to the embodiment of the orderly charging load prediction method for charging piles in the above-mentioned embodiments.

[0059] A mobile terminal implementing various embodiments of the present invention will now be described with reference to the accompanying drawings. In the subsequent description, suffixes such as "module", "component" or "unit" used to represent elements are used only to facilitate the description of the embodiments of the present invention and have no specific meaning in themselves. Therefore, "module" and "component" can be used interchangeably.

[0060] like Figure 2 As shown, the charging pile orderly charging load prediction system includes: A data acquisition module is used to acquire the electric vehicle operation data at different time points in the area to be predicted; A user portrait building module is used to build an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and form a user portrait for each electric vehicle, and finally form a user portrait set; The charging behavior prediction module is used to predict the time and location of electric vehicle charging behavior based on the user portrait set, and further predict the possibility of charging behavior and the required charging amount of each charging pile at different time points in combination with the location of each charging pile in the predicted area; The load curve fitting module is used to obtain the corresponding charging data of the charging pile according to whether the selected prediction date is a working day or a holiday, and perform regression calculation to fit the load curve of each charging pile; The prediction curve synthesis module is used to match the fitted charging pile load curve with the previously predicted possibility of charging behavior and the required charging amount of each charging pile at different time points, and finally fit it into the load prediction curve of each charging pile to realize the load prediction of each charging pile at different time points on the prediction date; The result display module is used to display the load forecast curve and corresponding data in the form of graphics and data. The present application also provides an electronic device for implementing various embodiments of the present invention, the electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor.

[0061] Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0062] Figure 3 A schematic diagram of the hardware structure of an electronic device for implementing various embodiments of the present invention.

[0063] The electronic device 500 includes, but is not limited to, components such as a processor 501, a network module 502, an audio output unit 503, an input unit 504, a display unit 506, a user input unit 507, an interface unit 508, and a memory 509. Those skilled in the art will appreciate that the electronic device structure involved in the embodiments of the present invention does not constitute a limitation on the electronic device, and the electronic device may include more or fewer components than shown in the figure, or combine certain components, or arrange the components differently.

[0064] In the embodiments of the present invention, electronic devices include but are not limited to laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. Electronic devices can also represent various forms of mobile devices and other similar computing devices. The components shown herein, their connections and relationships, and their functions are only examples and are not intended to limit the implementation of the embodiments of the present application described and / or required herein.

[0065] In the embodiment of the present application, the processor 501 can be implemented by using at least one of an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a processor, a controller, a microcontroller, a microprocessor, and an electronic unit designed to perform the functions described herein. In some cases, such an implementation can be implemented in a controller. For software implementation, implementations such as processes or functions can be implemented with separate software modules that allow execution of at least one function or operation. The software code can be implemented by a software application (or program) written in any appropriate programming language, and the software code can be stored in a memory and executed by a controller.

[0066] The display unit 506 is used to display information input by the user or information provided to the user. The display unit 506 may include a display panel, which may be configured in the form of a liquid crystal display (LCD), an organic light-emitting diode (OLED), etc.

[0067] The user input unit 507 may include, but is not limited to, a physical keyboard, function keys (such as a volume control key, a switch key, etc.), a trackball, a mouse, and a joystick, which will not be described in detail here.

[0068] The interface unit 508 is an interface for connecting external devices to the electronic device 500. For example, the external devices may include a wired or wireless headset port, an external power supply (or battery charger) port, a wired or wireless data port, a memory card port, a port for connecting a device with an identification module, an audio input / output (I / O) port, a video I / O port, an earphone port, and the like.

[0069] In addition, the electronic device 500 includes some functional modules not shown, which will not be described in detail here.

[0070] Those skilled in the art will appreciate that the various aspects of the electronic device provided by the present application may be implemented as a system, method or program product. Therefore, the various aspects of the present disclosure may be specifically implemented in the following forms, namely: a complete hardware implementation, a complete software implementation (including firmware, microcode, etc.), or an implementation combining hardware and software, which may be collectively referred to as a "circuit", "module" or "system" herein.

[0071] The present application also provides a storage medium, in which a program product capable of implementing a method for predicting the orderly charging load of a charging pile is stored. In some possible implementations, various aspects of the present disclosure may also be implemented in the form of a program product, which includes a program code, and when the program product is run on a terminal device, the program code is used to enable the terminal device to execute the steps described in the above "Exemplary Method" section of this specification according to various exemplary implementations of the present disclosure.

[0072] The storage medium can adopt any combination of one or more readable media. The readable medium can be a readable signal medium or a readable storage medium. The readable storage medium can be, for example, but not limited to, a system, device or device of electricity, magnetism, light, electromagnetic, infrared, or semiconductor, or any combination of the above. More specific examples (non-exhaustive list) of readable storage media include: an electrical connection with one or more wires, a portable disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above.

[0073] The above description of the disclosed embodiments enables one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention will not be limited to the embodiments shown herein, but rather to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for predicting the orderly charging load of a charging pile, characterized in that the steps include: S1. Obtaining electric vehicle operation data at different time points in the prediction area, and using the electric vehicle operation data to build an electric vehicle operation database; S2. Build an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and form a user portrait for each electric vehicle, and all user portraits form a user portrait set; S3. Predict the time and location of electric vehicle charging behavior based on the user portrait set, and predict the possibility of charging behavior and the required charging amount of each charging pile at different time points based on the location of each charging pile in the predicted area; S4. Select a forecast date and determine whether the forecast date is a working day or a holiday; when the forecast date is a working day, obtain the charging data of the charging piles in the forecast area on working days within a period of time, perform regression calculation on the charging data on working days, and fit a charging pile load curve that feedbacks the load of each charging pile over time within a day; When the prediction date is a holiday, the holiday charging data of the charging piles in the prediction area within a period of time is obtained, and regression calculation is performed on the holiday charging data to fit a charging pile load curve that reflects the load change of each charging pile over time within a day; S5. Match the charging pile load curve with the possibility of charging behavior and the required charging amount of each charging pile at different time points in step S3, and finally fit it into a charging pile load prediction curve for each charging pile, and predict the load of each charging pile at different time points on the prediction date.

2. The method for predicting the orderly charging load of a charging pile according to claim 1, characterized in that: In step S1, the electric vehicle operation data includes static operation data and dynamic data. The static data includes vehicle identification, vehicle purpose and / or vehicle energy consumption, and the dynamic operation data includes the vehicle's average daily mileage, the vehicle's average daily trip times, the vehicle's battery capacity, the vehicle's average charge per trip, the vehicle's charging status parameters, the vehicle's charging time point and / or the vehicle's charging location.

3. The method for predicting the orderly charging load of a charging pile according to claim 1, characterized in that: In step S2, the user portrait of the electric vehicle includes vehicle identification information, driving information and charging information; The vehicle identification information includes the vehicle number, vehicle model, vehicle VIN code, vehicle license plate number and / or vehicle purpose; Driving information includes monthly vehicle travel days, monthly vehicle travel mileage, and average vehicle travel mileage; The charging information includes the monthly charging amount of the vehicle, the monthly charging times of the vehicle, the average charging amount per vehicle, the distribution of vehicle charging time periods and / or the distribution of vehicle charging locations.

4. The method for predicting the orderly charging load of a charging pile according to claim 3, characterized in that: The user profile of the electric vehicle also includes vehicle usage flexibility information, and the vehicle usage flexibility information includes vehicle time flexibility, vehicle space flexibility, vehicle energy flexibility and / or vehicle management flexibility.

5. The method for predicting the orderly charging load of a charging pile according to claim 1, characterized in that: In step S1, each day is divided into 96 time points, with a 15-minute difference between two adjacent time points, and the electric vehicle operation data at each time point in the area to be predicted is obtained; In step S4, a day is divided into 96 time points, with a difference of 15 minutes between two adjacent time points. When the predicted date is a weekday, the charging data of the charging piles in the test area at each time point in the 10 working days before the predicted date is obtained to form the weekday charging data; when the predicted date is a holiday, the charging data of the charging piles in the test area at each time point in the 10 holidays before the predicted date is obtained to form the holiday charging data.

6. The method for predicting the orderly charging load of a charging pile according to claim 1, characterized in that: The method further includes step S6: aggregating the charging load prediction curves of all charging piles in the area to be predicted and fitting them into a total charging pile load prediction curve; The charging pile load prediction curves of the charging piles are divided according to the regions where the charging piles are located and the types of the charging piles. The charging pile load prediction curves of each region and each type are summarized and fitted to form regional charging pile load prediction curves for each region, classified charging pile load prediction curves for each type of charging pile and regional classified charging pile load prediction curves for each type of charging pile in each region.

7. The method for predicting the orderly charging load of a charging pile according to claim 6, characterized in that: The types of charging piles include public charging piles, dedicated charging piles and private charging piles, among which dedicated charging piles include bus-dedicated charging piles, logistics park-dedicated charging piles and / or airport-dedicated charging piles.

8. A charging pile orderly charging load prediction system, characterized in that: The method for predicting the orderly charging load of a charging pile according to any one of claims 1 to 7 comprises: A data acquisition module is used to acquire the electric vehicle operation data at different time points in the area to be predicted; A user portrait construction module is used to construct an electric vehicle user portrait classification model based on the data in the electric vehicle operation database, and form a user portrait of each electric vehicle, and finally form a user portrait set; The charging behavior prediction module is used to predict the time and location of electric vehicle charging behavior based on the user portrait set, and further predict the possibility of charging behavior and the required charging amount of each charging pile at different time points in combination with the location of each charging pile in the predicted area; The load curve fitting module is used to obtain the corresponding charging data of the charging pile according to whether the selected prediction date is a working day or a holiday, and perform regression calculation to fit the load curve of each charging pile; The prediction curve synthesis module is used to match the fitted charging pile load curve with the previously predicted possibility of charging behavior and the required charging amount of each charging pile at different time points, and finally fit it into the load prediction curve of each charging pile to realize the load prediction of each charging pile at different time points on the prediction date; The result display module is used to display the load forecast curve and corresponding data in the form of graphics and data.

9. An electronic device, characterized in that: The method comprises a memory, a processor and a computer program stored in the memory and executable on the processor, wherein the processor is used to implement the steps of the method for predicting the orderly charging load of a charging pile as described in any one of claims 1 to 7 when executing the computer program.

10. A storage medium, characterized in that: A computer program is stored on the storage medium, and when the computer program is executed by the processor, the steps of the method for predicting the orderly charging load of a charging pile as described in any one of claims 1 to 7 are implemented.

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