CN114239967A - Electric vehicle load prediction method, system and storage medium - Google Patents
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Abstract
The invention provides a method, a system and a storage medium for predicting electric vehicle load. The electric vehicles are classified, the electric vehicles are divided into a plurality of time periods every day, and then the charging load of the electric vehicles within the prediction date is predicted by calculating the data based on parameters such as the electric vehicle planning period prediction quantity, the electric vehicle charging probability of each time period in the reference period, the electric vehicle charging power and the like. The method has the advantages of strong universality and stability, simple implementation mode and accurate prediction result.
Description
Technical Field
The invention relates to the technical field of load prediction, in particular to a method, a system and a storage medium for predicting electric vehicle load.
Background
With the development of science and technology and economy, electric vehicles are gradually used by the public due to the advantages of high efficiency and energy conservation, and the electric vehicles as emerging loads put higher requirements on the optimal scheduling and safe operation of a power grid. Compared with the conventional load, the charging load of the electric automobile has the characteristics of randomness of service time, charging fluctuation and the like, and the conventional load prediction method is not suitable for electric automobile load prediction any more. The existing electric vehicle load prediction method is slightly lack of practicability, and operability needs to be improved.
Disclosure of Invention
The invention aims to solve the technical problem that the invention provides a method, a system and a storage medium for predicting the load of an electric vehicle, which have strong universality and stability, simple implementation mode and accurate prediction result.
In order to solve the above technical problem, an aspect of the present invention provides a method for predicting a load of an electric vehicle, including:
step S10, determining a reference period for statistics, and obtaining historical data of various electric vehicles in the reference period, wherein the historical data comprises quantity information, rated power information and charging frequency information of various electric vehicles in the reference period; the electric automobile comprises four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and is divided into a preset number of time periods every day;
step S11, according to the historical data of various electric vehicles, calculating and obtaining the equivalent quantity, the charging probability and the average power of various electric vehicles in each day time period in the reference period;
step S12, predicting and obtaining the predicted number of each type of electric automobile every day in the next prediction period according to the number change rate of each type of electric automobile in the historical data or/and the predicted total number of electric automobiles in the planning time interval;
step S13, calculating and obtaining the predicted load of each electric vehicle in each time period within the predicted date according to the charging probability and the average power of each electric vehicle in each time period per day within the reference period and the predicted quantity of each electric vehicle;
in step S14, the total predicted loads of all the electric vehicles in each time period within the prediction date and the total predicted loads of the prediction dates are obtained statistically.
Preferably, the step S10 further includes:
determining the previous year, the previous quarter and the previous month as a reference period, and acquiring historical data of various electric vehicles in the reference period; and counting the charging times of all the days of all the electric automobiles in the reference period, and carrying out averaging or weighting processing to obtain the charging times of all the time periods of all the electric automobiles.
Preferably, the step S11 further includes:
step S110, calculating and obtaining the equivalent quantity of various types of electric vehicles in the reference period according to the following formula:
the equivalent number of electric vehicles (total number of such electric vehicles at the end of the reference period + total number of such electric vehicles at the beginning of the reference period)/2;
step S111, calculating and obtaining the charging probability of each electric vehicle in each time period of each day in the reference period according to the following formula:
the charging probability of each time period is the charging times of the electric vehicles in the time period/the equivalent quantity of the electric vehicles in the time period;
step S112, calculating and obtaining the average power of various electric vehicles in the reference period according to the following formula;
the average power of the electric vehicle is ∑ rated power of the electric vehicle of this type/equivalent number of the electric vehicles of this type.
Preferably, step S12 further includes:
according to the quantity change rate of various electric vehicles in the historical data, the total number of various electric vehicles at the end of the reference period is combined, and the predicted quantity of various electric vehicles every day in the next prediction period is obtained; or
According to the target total number of various electric vehicles at the end of the planning period, the total number of various electric vehicles at the end of the reference period is combined, and the predicted number of various electric vehicles in the next prediction period is obtained; or
The larger of the two prediction numbers is determined as the final prediction number.
Preferably, the step S13 further includes:
step S13, calculating according to the charging probability and the average power of each electric vehicle in each time period of each day in the reference period and the predicted quantity of each electric vehicle through the following formula, and obtaining the predicted load of each electric vehicle in each time period in the predicted date;
the predicted charging load of such electric vehicles at a certain time is the charging probability of such electric vehicles at a certain time and the predicted number of such electric vehicles is the average power of such electric vehicles.
Preferably, the step S14 further includes:
summarizing the predicted charging loads of all types of electric automobiles in each time period within the prediction date to obtain the total predicted load of all electric automobiles in each time period within the prediction date;
and summarizing the total predicted loads in all time periods in the prediction date to obtain the total predicted loads of all electric vehicles in the prediction date.
Accordingly, in another aspect of the present invention, there is also provided an electric vehicle load prediction system, including:
the system comprises a reference data obtaining unit, a charging time counting unit and a charging time counting unit, wherein the reference data obtaining unit is used for determining a reference period for counting and obtaining historical data of various types of electric vehicles in the reference period, and the historical data comprises quantity information, rated power information and charging time information of various time periods in each day; the electric automobile comprises four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and is divided into a preset number of time periods every day;
the reference parameter calculation unit is used for calculating and obtaining the equivalent quantity of various electric automobiles in a reference period, the charging probability of each day and the average power according to the historical data of various electric automobiles;
the electric vehicle quantity prediction unit is used for predicting and obtaining the predicted quantity of each type of electric vehicle every day in the next prediction period according to the quantity change rate of each type of electric vehicle in the historical data or/and the total predicted vehicle quantity in the planning time period of the electric vehicle;
the time interval load prediction unit is used for calculating and obtaining the predicted load of each time interval of each type of electric automobile in the prediction date according to the charging probability and the average power of each time interval of each day of each type of electric automobile in the reference period and the predicted quantity of each type of electric automobile;
and the predicted load counting unit is used for counting and obtaining the total predicted load of all the electric automobiles in each time period within the prediction date and the total predicted load of the prediction date.
As yet another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a series of computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to implement the foregoing method.
The embodiment of the invention has the following beneficial effects:
the invention provides a method, a system and a storage medium for predicting electric vehicle load. Firstly, determining a reference period for statistics, and obtaining historical data of various electric vehicles in the reference period, wherein the historical data comprises quantity information, rated power information and charging frequency information of various electric vehicles in the reference period; then, according to historical data of various electric vehicles, calculating and obtaining the equivalent quantity of various electric vehicles in a reference period, the charging probability of each time period of each day and the average power; then, predicting and obtaining the predicted quantity of each type of electric automobile every day in the next prediction period according to the quantity change rate of each type of electric automobile in the historical data or/and the total predicted quantity of the electric automobiles in the planning time period; calculating and obtaining the predicted load of each electric vehicle in each time period within the prediction date according to the charging probability and the average power of each electric vehicle in each time period per day within the reference period and the predicted quantity of each electric vehicle; finally, counting to obtain the total predicted load of all the electric automobiles in each time period within the predicted date and the total predicted load of the predicted date; the method has the characteristics of simple and clear operation; and has stronger universality and stability;
in the embodiment of the invention, electric vehicles are classified, the electric vehicles are divided into a plurality of time periods every day, and then the charging load of the electric vehicles in the prediction date is predicted by calculating the data based on the parameters such as the planned period prediction quantity of the electric vehicles, the charging probability of each time period of the electric vehicles in the reference period, the charging power of the electric vehicles and the like, so that the accuracy of the prediction result can be improved, and more effective support is provided for the load prediction of a power grid.
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In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is within the scope of the present invention for those skilled in the art to obtain other drawings based on the drawings without inventive exercise.
Fig. 1 is a schematic main flow chart of an embodiment of a method for predicting a load of an electric vehicle according to the present invention;
fig. 2 is a schematic structural diagram of an embodiment of a load prediction system of an electric vehicle according to the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 shows a main flow chart of an embodiment of a method for predicting a load of an electric vehicle according to the present invention. In this embodiment, the method includes the steps of:
step S10, determining a reference period for statistics, and obtaining historical data of various electric vehicles in the reference period, wherein the historical data comprises quantity information, rated power information and charging frequency information of various electric vehicles in the reference period; the category of the electric automobile includes four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and each day is divided into a predetermined number of time periods, for example, in one example, one day can be divided into 6 or 12 time periods;
in a specific example, the step S10 further includes:
determining the previous year, the previous quarter and the previous month as a reference period, and acquiring historical data of various electric vehicles in the reference period; counting the charging times of all the electric vehicles in all the days in the reference period and carrying out averaging or weighting processing to obtain the charging times of all the electric vehicles in all the time periods; the charging times of each time period of each type of electric automobile can be directly replaced by the number of the charging vehicles in each time period.
It can be understood that, the determination of the reference period can be selected according to actual conditions, and the closer the reference period is to the current time, the more accurate the last predicted data is;
meanwhile, the charging times of all the days and time periods of various electric vehicles in the reference period can be obtained by averaging or adding each value by multiplying a weight, in one example, the closer to the current time, the larger the weight is, and all the weights are added to be equal to 1.
Step S11, according to the historical data of various electric vehicles, calculating and obtaining the equivalent quantity, the charging probability and the average power of various electric vehicles in each day time period in the reference period;
in a specific example, the step S11 further includes:
step S110, calculating and obtaining the equivalent quantity of various types of electric vehicles in the reference period according to the following formula:
the equivalent number of electric vehicles (total number of such electric vehicles at the end of the reference period + total number of such electric vehicles at the beginning of the reference period)/2;
step S111, calculating and obtaining the charging probability of each electric vehicle in each time period of each day in the reference period according to the following formula:
the charging probability of each time period is the charging times of the electric vehicles in the time period/the equivalent quantity of the electric vehicles in the time period;
step S112, calculating and obtaining the average power of various electric vehicles in the reference period according to the following formula;
the average power of the electric vehicle is ∑ rated power of the electric vehicle of this type/equivalent number of the electric vehicles of this type. Wherein, the power rating of the Σ electric vehicle means that all power ratings of the electric vehicles are added;
step S12, predicting and obtaining the predicted number of each type of electric automobile every day in the next prediction period according to the number change rate of each type of electric automobile in the historical data or/and the predicted total number of electric automobiles in the planning time interval;
in a specific example, the step S12 further includes:
according to the quantity change rate of various electric vehicles in the historical data, the total number of various electric vehicles at the end of the reference period is combined, and the predicted quantity of various electric vehicles every day in the next prediction period is obtained; or
According to the target total number of various electric vehicles at the end of the planning period, the total number of various electric vehicles at the end of the reference period is combined, and the predicted number of various electric vehicles in the next prediction period is obtained; or
The larger of the two prediction numbers is determined as the final prediction number.
Wherein the planning period may be, for example, a piece or half a year; the predicted period may be one or more days.
Step S13, calculating and obtaining the predicted load of each electric vehicle in each time period within the predicted date according to the charging probability and the average power of each electric vehicle in each time period per day within the reference period and the predicted quantity of each electric vehicle;
in a specific example, the step S13 further includes:
step S13, calculating according to the charging probability and the average power of each electric vehicle in each time period of each day in the reference period and the predicted quantity of each electric vehicle through the following formula, and obtaining the predicted load of each electric vehicle in each time period in the predicted date;
the predicted charging load of such electric vehicles at a certain time is the charging probability of such electric vehicles at a certain time and the predicted number of such electric vehicles is the average power of such electric vehicles.
In step S14, the total predicted loads of all the electric vehicles in each time period within the prediction date and the total predicted loads of the prediction dates are obtained statistically.
In a specific example, the step S14 further includes:
summarizing the predicted charging loads of all types of electric automobiles in each time period within the prediction date to obtain the total predicted load of all electric automobiles in each time period within the prediction date; in each time interval, adding the predicted charging loads of the electric bus, the electric private car, the electric taxi and the electric business car calculated in the step S13 in each time interval to obtain a total predicted load in the time interval;
and summarizing the total predicted loads in all time periods in the prediction date to obtain the total predicted loads of all electric vehicles in the prediction date.
The method provided by the invention can be understood as based on the parameters such as the electric vehicle planning period prediction quantity, the electric vehicle charging probability of each time period in the reference period, the electric vehicle charging power and the like, and the data are calculated, so that the charging load of the electric vehicle can be predicted within the prediction date, and more effective support is provided for the power grid load prediction. The method is very simple, has strong universality and stability, and has accurate prediction results.
Accordingly, in another aspect of the present invention, there is also provided an electric vehicle load prediction system, including:
the system comprises a reference data obtaining unit, a charging time counting unit and a charging time counting unit, wherein the reference data obtaining unit is used for determining a reference period for counting and obtaining historical data of various types of electric vehicles in the reference period, and the historical data comprises quantity information, rated power information and charging time information of various time periods in each day; the electric automobile comprises four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and is divided into a preset number of time periods every day;
the reference parameter calculation unit is used for calculating and obtaining the equivalent quantity of various electric automobiles in a reference period, the charging probability of each day and the average power according to the historical data of various electric automobiles;
the electric vehicle quantity prediction unit is used for predicting and obtaining the predicted quantity of each type of electric vehicle every day in the next prediction period according to the quantity change rate of each type of electric vehicle in the historical data or/and the total predicted vehicle quantity in the planning time period of the electric vehicle;
the time interval load prediction unit is used for calculating and obtaining the predicted load of each time interval of each type of electric automobile in the prediction date according to the charging probability and the average power of each time interval of each day of each type of electric automobile in the reference period and the predicted quantity of each type of electric automobile;
and the predicted load counting unit is used for counting and obtaining the total predicted load of all the electric automobiles in each time period within the prediction date and the total predicted load of the prediction date.
Further details may be referred to and incorporated in the description of fig. 1 and will not be repeated here.
As yet another aspect of the present invention, there is also provided a computer-readable storage medium having stored thereon a series of computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to implement the foregoing method.
Further details may be referred to and incorporated in the description of fig. 1 and will not be repeated here.
The embodiment of the invention has the following beneficial effects:
the invention provides a method, a system and a storage medium for predicting electric vehicle load. Firstly, determining a reference period for statistics, and obtaining historical data of various electric vehicles in the reference period, wherein the historical data comprises quantity information, rated power information and charging frequency information of various electric vehicles in the reference period; then, according to historical data of various electric vehicles, calculating and obtaining the equivalent quantity of various electric vehicles in a reference period, the charging probability of each time period of each day and the average power; then, predicting and obtaining the predicted quantity of each type of electric automobile every day in the next prediction period according to the quantity change rate of each type of electric automobile in the historical data or/and the total predicted quantity of the electric automobiles in the planning time period; calculating and obtaining the predicted load of each electric vehicle in each time period within the prediction date according to the charging probability and the average power of each electric vehicle in each time period per day within the reference period and the predicted quantity of each electric vehicle; finally, counting to obtain the total predicted load of all the electric automobiles in each time period within the predicted date and the total predicted load of the predicted date; the method has the characteristics of simple and clear operation; and has stronger universality and stability;
in the embodiment of the invention, electric vehicles are classified, the electric vehicles are divided into a plurality of time periods every day, and then the charging load of the electric vehicles in the prediction date is predicted by calculating the data based on the parameters such as the planned period prediction quantity of the electric vehicles, the charging probability of each time period of the electric vehicles in the reference period, the charging power of the electric vehicles and the like, so that the accuracy of the prediction result can be improved, and more effective support is provided for the load prediction of a power grid.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, apparatus, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
The present invention is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
While the invention has been described in connection with what is presently considered to be the most practical and preferred embodiment, it is to be understood that the invention is not to be limited to the disclosed embodiment, but on the contrary, is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (8)
1. The electric vehicle load prediction method is characterized by comprising the following steps:
step S10, determining a reference period for statistics, and obtaining historical data of various electric vehicles in the reference period, wherein the historical data comprises quantity information, rated power information and charging frequency information of various electric vehicles in the reference period; the electric automobile comprises four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and is divided into a preset number of time periods every day;
step S11, according to the historical data of various electric vehicles, calculating and obtaining the equivalent quantity, the charging probability and the average power of various electric vehicles in each day time period in the reference period;
step S12, predicting and obtaining the predicted number of each type of electric automobile every day in the next prediction period according to the number change rate of each type of electric automobile in the historical data or/and the predicted total number of electric automobiles in the planning time interval;
step S13, calculating and obtaining the predicted load of each electric vehicle in each time period within the predicted date according to the charging probability and the average power of each electric vehicle in each time period per day within the reference period and the predicted quantity of each electric vehicle;
in step S14, the total predicted loads of all the electric vehicles in each time period within the prediction date and the total predicted loads of the prediction dates are obtained statistically.
2. The method of claim 1, wherein the step S10 further comprises:
determining the previous year, the previous quarter and the previous month as a reference period, and acquiring historical data of various electric vehicles in the reference period; and counting the charging times of all the days of all the electric automobiles in the reference period, and carrying out averaging or weighting processing to obtain the charging times of all the time periods of all the electric automobiles.
3. The method of claim 2, wherein the step S11 further comprises:
step S110, calculating and obtaining the equivalent quantity of various types of electric vehicles in the reference period according to the following formula:
the equivalent number of electric vehicles (total number of such electric vehicles at the end of the reference period + total number of such electric vehicles at the beginning of the reference period)/2;
step S111, calculating and obtaining the charging probability of each electric vehicle in each time period of each day in the reference period according to the following formula:
the charging probability of each time period is the charging times of the electric vehicles in the time period/the equivalent quantity of the electric vehicles in the time period;
step S112, calculating and obtaining the average power of various electric vehicles in the reference period according to the following formula;
the average power of the electric vehicle is ∑ rated power of the electric vehicle of this type/equivalent number of the electric vehicles of this type.
4. The method of claim 3, wherein the step S12 further comprises:
according to the quantity change rate of various electric vehicles in the historical data, the total number of various electric vehicles at the end of the reference period is combined, and the predicted quantity of various electric vehicles every day in the next prediction period is obtained; or
According to the target total number of various electric vehicles at the end of the planning period, the total number of various electric vehicles at the end of the reference period is combined, and the predicted number of various electric vehicles in the next prediction period is obtained; or
The larger of the two prediction numbers is determined as the final prediction number.
5. The method of claim 4, wherein the step S13 further comprises:
step S13, calculating according to the charging probability and the average power of each electric vehicle in each time period of each day in the reference period and the predicted quantity of each electric vehicle through the following formula, and obtaining the predicted load of each electric vehicle in each time period in the predicted date;
the predicted charging load of such electric vehicles at a certain time is the charging probability of such electric vehicles at a certain time and the predicted number of such electric vehicles is the average power of such electric vehicles.
6. The method of claim 5, wherein the step S14 further comprises:
summarizing the predicted charging loads of all types of electric automobiles in each time period within the prediction date to obtain the total predicted load of all electric automobiles in each time period within the prediction date;
and summarizing the total predicted loads in all time periods in the prediction date to obtain the total predicted loads of all electric vehicles in the prediction date.
7. An electric vehicle load prediction system, comprising:
the system comprises a reference data obtaining unit, a charging time counting unit and a charging time counting unit, wherein the reference data obtaining unit is used for determining a reference period for counting and obtaining historical data of various types of electric vehicles in the reference period, and the historical data comprises quantity information, rated power information and charging time information of various time periods in each day; the electric automobile comprises four categories, namely an electric bus, an electric private car, an electric taxi and an electric official car, and is divided into a preset number of time periods every day;
the reference parameter calculation unit is used for calculating and obtaining the equivalent quantity of various electric automobiles in a reference period, the charging probability of each day and the average power according to the historical data of various electric automobiles;
the electric vehicle quantity prediction unit is used for predicting and obtaining the predicted quantity of each type of electric vehicle every day in the next prediction period according to the quantity change rate of each type of electric vehicle in the historical data or/and the total predicted vehicle quantity in the planning time period of the electric vehicle;
the time interval load prediction unit is used for calculating and obtaining the predicted load of each time interval of each type of electric automobile in the prediction date according to the charging probability and the average power of each time interval of each day of each type of electric automobile in the reference period and the predicted quantity of each type of electric automobile;
and the predicted load counting unit is used for counting and obtaining the total predicted load of all the electric automobiles in each time period within the prediction date and the total predicted load of the prediction date.
8. A computer-readable storage medium having stored thereon a series of computer-executable instructions that, when executed by one or more computing devices, cause the one or more computing devices to implement a method as in any one of claims 1-6.
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