CN116187181A - Electric vehicle charging load time sequence modeling method, system and medium based on width learning system - Google Patents
Electric vehicle charging load time sequence modeling method, system and medium based on width learning system Download PDFInfo
- Publication number
- CN116187181A CN116187181A CN202310099216.7A CN202310099216A CN116187181A CN 116187181 A CN116187181 A CN 116187181A CN 202310099216 A CN202310099216 A CN 202310099216A CN 116187181 A CN116187181 A CN 116187181A
- Authority
- CN
- China
- Prior art keywords
- electric vehicle
- charging load
- time sequence
- charging
- width learning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 54
- 238000010606 normalization Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 18
- 238000012549 training Methods 0.000 claims description 12
- 230000006870 function Effects 0.000 claims description 10
- 238000013507 mapping Methods 0.000 claims description 9
- 238000006243 chemical reaction Methods 0.000 claims description 3
- 230000005284 excitation Effects 0.000 claims description 3
- 239000011159 matrix material Substances 0.000 claims description 3
- 238000012360 testing method Methods 0.000 claims description 3
- 238000011161 development Methods 0.000 abstract description 6
- 238000004458 analytical method Methods 0.000 abstract description 3
- 238000004590 computer program Methods 0.000 description 7
- 238000010586 diagram Methods 0.000 description 7
- 230000008569 process Effects 0.000 description 7
- 238000010801 machine learning Methods 0.000 description 4
- 230000009286 beneficial effect Effects 0.000 description 3
- 230000005540 biological transmission Effects 0.000 description 2
- 229910052799 carbon Inorganic materials 0.000 description 2
- 230000008859 change Effects 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 238000004519 manufacturing process Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000003287 optical effect Effects 0.000 description 2
- 230000000306 recurrent effect Effects 0.000 description 2
- OKTJSMMVPCPJKN-UHFFFAOYSA-N Carbon Chemical compound [C] OKTJSMMVPCPJKN-UHFFFAOYSA-N 0.000 description 1
- 238000000342 Monte Carlo simulation Methods 0.000 description 1
- 238000013528 artificial neural network Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000004134 energy conservation Methods 0.000 description 1
- 230000006872 improvement Effects 0.000 description 1
- 238000012417 linear regression Methods 0.000 description 1
- 238000007637 random forest analysis Methods 0.000 description 1
- 230000009467 reduction Effects 0.000 description 1
- 230000003014 reinforcing effect Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 230000003068 static effect Effects 0.000 description 1
- 238000012795 verification Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/10—Complex mathematical operations
- G06F17/18—Complex mathematical operations for evaluating statistical data, e.g. average values, frequency distributions, probability functions, regression analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/15—Vehicle, aircraft or watercraft design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/12—Timing analysis or timing optimisation
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02T—CLIMATE CHANGE MITIGATION TECHNOLOGIES RELATED TO TRANSPORTATION
- Y02T10/00—Road transport of goods or passengers
- Y02T10/60—Other road transportation technologies with climate change mitigation effect
- Y02T10/70—Energy storage systems for electromobility, e.g. batteries
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Analysis (AREA)
- General Engineering & Computer Science (AREA)
- Evolutionary Computation (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Computational Mathematics (AREA)
- Computer Hardware Design (AREA)
- Mathematical Physics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Evolutionary Biology (AREA)
- Automation & Control Theory (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Bioinformatics & Cheminformatics (AREA)
- Bioinformatics & Computational Biology (AREA)
- Artificial Intelligence (AREA)
- Aviation & Aerospace Engineering (AREA)
- Operations Research (AREA)
- Probability & Statistics with Applications (AREA)
- Algebra (AREA)
- Medical Informatics (AREA)
- Databases & Information Systems (AREA)
- Supply And Distribution Of Alternating Current (AREA)
- Charge And Discharge Circuits For Batteries Or The Like (AREA)
Abstract
The application relates to an electric vehicle charging load time sequence modeling method, system and medium based on a width learning system, wherein the method comprises the following specific steps: the method comprises the following steps of (1) determining an electric vehicle charging load influence factor; (2) normalizing the data related to the charging load of the electric vehicle; (3) Establishing an electric vehicle charging load time sequence prediction model based on a width learning system; and (4) time sequence prediction of the charging load of the electric vehicle. The method and the device adopt data normalization processing, so that the influence of different dimensions among the original data influencing the charging load of the electric vehicle can be avoided; according to the performance analysis of the charging load of the electric vehicle, the factors influencing the charging load of the electric vehicle are determined, a width learning system is adopted to establish a charging load time sequence prediction model of the electric vehicle, accurate time sequence prediction of the charging load of the electric vehicle can be achieved, support is provided for further improving the charging efficiency of the electric vehicle, and a foundation is laid for achieving green energy-saving development of the intelligent power grid.
Description
Technical Field
The application relates to the technical field of intelligent power grid application, in particular to an electric vehicle charging load time sequence modeling method, system and medium based on a width learning system.
Background
Along with the increasing global call for energy conservation, emission reduction and green manufacturing, the development of green low carbon has become a major trend of global development. Due to the low-carbon and environmental-protection advantages of Electric Vehicles (EVs), more and more countries are beginning to popularize electric vehicles. However, the prediction of the charging load of an electric vehicle is a key to ensure the safety and stability of the power grid. In addition, the charging load has the characteristics of nonlinearity, randomness, intermittence and the like. How to build an accurate electric vehicle charging load time series prediction model is important.
Currently, there are mainly conventional prediction methods and machine learning-based methods for research on charge load prediction. The traditional prediction method mainly comprises the following steps: linear regression, mathematical statistics, and the like. For example, a Monte Carlo simulation method is used to build a charging station load prediction model. The traditional modeling method is mostly based on experience for modeling and is not accurate. At the same time, a large amount of different types of data are required for model verification, which may lead to poor generalization effects.
In recent years, machine learning methods have begun to receive attention from more and more expert students and are applied to a wide variety of fields. The charging load prediction method based on machine learning is also a hot topic in the field of smart grids. For example, some expert scholars have established a prediction method based on a combination of improved random forests and density clusters to predict the short-term load frequency domain, but the model structure is relatively complex. Three time modeling methods, recurrent neural networks, long-short-term memory (LSTM) and gated recurrent units, are used to predict the loading of bus charging stations. The method for predicting the charging load of the electric automobile based on layered modeling is provided, and the prediction of the day and the previous hour is performed on one region of the Netherlands. An ultra-short-term electric vehicle charging load prediction model based on LSTM is developed. The modeling method based on machine learning can mine time sequence characteristics in the load change sequence without manually setting a large number of parameters, and is simple and effective.
Under the background, in order to realize the optimal scheduling of the charging load of the electric vehicle, an accurate time sequence prediction model needs to be established on the premise, so the invention provides an electric vehicle charging load time sequence modeling method based on a width learning system. The modeling method can consider the influence of different factors on the charging load and the time sequence characteristics of the charging load.
Disclosure of Invention
An aim of the embodiment of the application is to provide an electric vehicle charging load time sequence modeling method, system and medium based on a width learning system, which provide support for further improving the charging efficiency of the electric vehicle and further lay a foundation for realizing green energy-saving development of a smart grid.
In order to achieve the above purpose, the present application provides the following technical solutions:
in a first aspect, an embodiment of the present application provides a method for modeling a charging load time sequence of an electric vehicle based on a width learning system, including the following specific steps:
(1) Determination of electric vehicle charging load influencing factors
Analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle as charging time, charging power, holidays, weather and temperature;
(2) Electric vehicle charging load related data normalization processing
Collecting relevant data of the charging load of the electric vehicle in an actual field, carrying out normalization processing on all original data by utilizing a normalization formula to obtain relevant data of the charging load of the electric vehicle after normalization, and obtaining 358 groups of data altogether, wherein the first 258 groups of sample data are subjected to model training, and the rest 100 groups of data are subjected to model testing;
(3) Electric vehicle charging load time sequence prediction model establishment based on width learning system
Taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
(4) Time sequence prediction of charging load of electric vehicle
Inputting the factors influencing the charging load of the electric vehicle in the normalized data in the step (2) into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
The method for establishing the electric vehicle charging load time sequence prediction model based on the width learning system specifically comprises the following steps of:
(1) The training sample data set consisting of factors influencing the charging load of the electric vehicle and the charging load of the electric vehicle is as follows
As the o training sample, the input of the electric vehicle charging load time sequence prediction model is Y o Charging load for the o electric vehicle, N representing the total number of samples of the training sample data set; input data set
Next, input dataset +.>
Obtaining a series of feature nodes through feature mapping, wherein the ith group of feature nodes are as follows:
wherein ψ is the excitation function;
is a weight vector; />
Is a bias vector; m represents feature mapping times and is a positive integer greater than or equal to 1; input data set +.>
The method is obtained through m times of feature mapping conversion:
F m ＝[F 1 ,F 2 ,...,F m ].
(2)F m for connection to the enhancement node layer, the output of the j-th enhancement node is:
where xi is the hyperbolic tangent function,
is the bias of random initialization, +.>
Is a randomly initialized weight, n is the number of enhanced nodes; all the enhancement nodes are:
E n ＝[E 1 ,E 2 ,...,E n ].
(3) All feature nodes and enhancement nodes are connected to the output of the width learning model, and the output of the width learning model is:
Y＝[F 1 ,F 2 ,...,F m |E 1 ,E 2 ,...,E n ]W＝[F m |E n ]W
wherein W is the output weight of the width learning model; the output weight W is obtained by pseudo-inverse ridge regression approximation, namely:
W＝(ΩΩ T +ηI) -1 Ω T Y,
wherein η is a ridge parameter; i is an identity matrix; y is a time sequence predicted value set of the charging load of the electric vehicle.
In a second aspect, embodiments of the present application provide an electric vehicle charging load timing modeling system based on a width learning system, comprising,
the influence factor determination module is configured to determine,
the method comprises the steps of analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle to be charging time, charging power, holidays, weather and temperature;
the data normalization processing module is used for performing data normalization processing on the data,
normalizing all the original data by using a normalization formula to obtain normalized related data of the charging load of the electric vehicle;
a time sequence prediction model establishment module is used for establishing a time sequence prediction model,
taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
a timing prediction module configured to predict a timing of the first signal,
and inputting the factors influencing the charging load of the electric vehicle in the normalized data into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
In a third aspect, embodiments of the present application provide a computer readable storage medium storing program code that, when executed by a processor, implements the steps of the electric vehicle charging load timing modeling method based on a width learning system as described above.
Compared with the prior art, the beneficial effects of this application are: the method and the device adopt data normalization processing, so that the influence of different dimensions among the original data influencing the charging load of the electric vehicle can be avoided; according to the performance analysis of the charging load of the electric vehicle, the factors influencing the charging load of the electric vehicle are determined, a width learning system is adopted to establish a charging load time sequence prediction model of the electric vehicle, accurate time sequence prediction of the charging load of the electric vehicle can be achieved, support is provided for further improving the charging efficiency of the electric vehicle, and a foundation is laid for achieving green energy-saving development of the intelligent power grid.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments of the present application will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and should not be considered as limiting the scope, and other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an electric vehicle charging load time sequence modeling method based on a width learning system in an embodiment of the invention;
FIG. 2 is a block diagram of an electric vehicle charging load time sequence modeling system based on a width learning system in an embodiment of the invention;
FIG. 3 is a graph showing a comparison between a predicted result and an actual value of a charging load time sequence of an electric vehicle according to an embodiment of the present invention;
fig. 4 is a graph of relative error results of a prediction result of charging load time sequence of an electric vehicle according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application. It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further definition or explanation thereof is necessary in the following figures.
The terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, the method for modeling the charging load time sequence of the electric vehicle based on the width learning system comprises the following specific steps:
(1) Determination of electric vehicle charging load influencing factors
Analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle as charging time, charging power, holidays, weather and temperature;
(2) Electric vehicle charging load related data normalization processing
Collecting relevant data of the charging load of the electric vehicle in an actual field, carrying out normalization processing on all original data by utilizing a normalization formula to obtain relevant data of the charging load of the electric vehicle after normalization, and obtaining 358 groups of data altogether, wherein the first 258 groups of sample data are subjected to model training, and the rest 100 groups of data are subjected to model testing;
(3) Electric vehicle charging load time sequence prediction model establishment based on width learning system
Taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
(4) Time sequence prediction of charging load of electric vehicle
Inputting the factors influencing the charging load of the electric vehicle in the normalized data in the step (2) into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
The method for establishing the electric vehicle charging load time sequence prediction model based on the width learning system specifically comprises the following steps of:
(1) The training sample data set consisting of factors influencing the charging load of the electric vehicle and the charging load of the electric vehicle is as follows
As the o training sample, the input of the electric vehicle charging load time sequence prediction model is Y o Charging load for the o electric vehicle, N representing the total number of samples of the training sample data set; input data set
Next, input dataset +.>
Obtaining a series of feature nodes through feature mapping, wherein the ith group of feature nodes are as follows:
wherein ψ is the excitation function;
is a weight vector; />
Is a bias vector; m represents feature mapping times and is a positive integer greater than or equal to 1; input data set +.>
The method is obtained through m times of feature mapping conversion:
F m ＝[F 1 ,F 2 ,...,F m ].
(2)F m for connection to reinforcing jointsThe output of the j-th enhancement node is as follows:
where xi is the hyperbolic tangent function,
is the bias of random initialization, +.>
Is a randomly initialized weight, n is the number of enhanced nodes; all the enhancement nodes are:
E n ＝[E 1 ,E 2 ,...,E n ].
(3) All feature nodes and enhancement nodes are connected to the output of the width learning model, and the output of the width learning model is:
Y＝[F 1 ,F 2 ,...,F m |E 1 ,E 2 ,...,E n ]W＝[F m |E n ]W
wherein W is the output weight of the width learning model; the output weight W is obtained by pseudo-inverse ridge regression approximation, namely:
W＝(ΩΩ T +ηI) -1 Ω T Y,
wherein η is a ridge parameter; i is an identity matrix; y is a time sequence predicted value set of the charging load of the electric vehicle.
As shown in fig. 2, an electric vehicle charging load timing modeling system based on a width learning system includes,
the influencing factor determination module 1,
the method comprises the steps of analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle to be charging time, charging power, holidays, weather and temperature;
the data normalization processing module 2,
normalizing all the original data by using a normalization formula to obtain normalized related data of the charging load of the electric vehicle;
the time-series prediction model establishment module 3,
taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
the timing prediction module 4,
and inputting the factors influencing the charging load of the electric vehicle in the normalized data into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
Comparing the predicted value of the charging load time sequence of the electric vehicle with the actual value to obtain a result shown in figure 3;
as can be seen from fig. 3, the predicted value of the electric vehicle charging load is very close to the sample value. In addition, as shown in FIG. 4, the result of the prediction error between the predicted value and the actual value of the charging load time sequence of the electric vehicle is shown in FIG. 4, and the prediction error of the predicted value of the charging load of the electric vehicle is within [ -80,20] KWh. Therefore, the width learning system-based electric vehicle charging load time sequence prediction model can accurately and effectively predict the electric vehicle charging load according to the time sequence, and is beneficial to pushing the optimal scheduling of the electric vehicle charging load
The beneficial effects of the invention are as follows: by adopting data normalization processing, the influence of different dimensions among the original data influencing the charging load of the electric vehicle can be avoided; according to the performance analysis of the charging load of the electric vehicle, the factors influencing the charging load of the electric vehicle are determined, a width learning system is adopted to establish a charging load time sequence prediction model of the electric vehicle, accurate time sequence prediction of the charging load of the electric vehicle can be achieved, support is provided for further improving the charging efficiency of the electric vehicle, and a foundation is laid for achieving green energy-saving development of the intelligent power grid.
A computer readable storage medium storing program code which, when executed by a processor, implements the steps of a width learning system based electric vehicle charging load timing modeling method of claim 1 or 2.
It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application 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 application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations 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.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.
Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.
It should also be noted that the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article or apparatus that comprises an element.
The foregoing is merely exemplary embodiments of the present application and is not intended to limit the scope of the present application, and various modifications and variations may be suggested to one skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.
Claims (4)
1. The electric vehicle charging load time sequence modeling method based on the width learning system is characterized by comprising the following specific steps of:
(1) Determination of electric vehicle charging load influencing factors
Analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle as charging time, charging power, holidays, weather and temperature;
(2) Electric vehicle charging load related data normalization processing
Collecting relevant data of the charging load of the electric vehicle in an actual field, carrying out normalization processing on all original data by utilizing a normalization formula to obtain relevant data of the charging load of the electric vehicle after normalization, and obtaining 358 groups of data altogether, wherein the first 258 groups of sample data are subjected to model training, and the rest 100 groups of data are subjected to model testing;
(3) Electric vehicle charging load time sequence prediction model establishment based on width learning system
Taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
(4) Time sequence prediction of charging load of electric vehicle
Inputting the factors influencing the charging load of the electric vehicle in the normalized data in the step (2) into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
2. The method for modeling the charging load time sequence of the electric vehicle based on the width learning system according to claim 1, wherein the method for building the charging load time sequence prediction model of the electric vehicle based on the width learning system is specifically as follows:
(1) The training sample data set consisting of factors influencing the charging load of the electric vehicle and the charging load of the electric vehicle is as follows
As the o training sample, the input of the electric vehicle charging load time sequence prediction model is Y o Charging load for the o electric vehicle, N representing the total number of samples of the training sample data set; input data set
Next, input dataset +.>
Obtaining a series of feature nodes through feature mapping, wherein the ith group of feature nodes are as follows:
wherein ψ is the excitation function;
is a weight vector; />
Is a bias vector; m represents feature mapping times and is a positive integer greater than or equal to 1; input data set +.>
The method is obtained through m times of feature mapping conversion:
F m ＝[F 1 ,F 2 ,...,F m ].
(2)F m for connection to the enhancement node layer, the output of the j-th enhancement node is:
where xi is the hyperbolic tangent function,
is the bias of random initialization, +.>
Is a randomly initialized weight, n is the number of enhanced nodes; all the enhancement nodes are:
E n ＝[E 1 ,E 2 ,...,E n ].
(3) All feature nodes and enhancement nodes are connected to the output of the width learning model, and the output of the width learning model is:
Y＝[F 1 ,F 2 ,...,F m |E 1 ,E 2 ,...,E n ]W＝[F m |E n ]W
wherein W is the output weight of the width learning model; the output weight W is obtained by pseudo-inverse ridge regression approximation, namely:
W＝(ΩΩ T +ηI) -1 Ω T Y,
wherein η is a ridge parameter; i is an identity matrix; y is a time sequence predicted value set of the charging load of the electric vehicle.
3. A width learning system-based electric vehicle charging load time sequence modeling system is characterized by comprising,
the influence factor determination module is configured to determine,
the method comprises the steps of analyzing according to the charging performance of the electric vehicle, and determining the factors influencing the charging load of the electric vehicle to be charging time, charging power, holidays, weather and temperature;
the data normalization processing module is used for performing data normalization processing on the data,
normalizing all the original data by using a normalization formula to obtain normalized related data of the charging load of the electric vehicle;
a time sequence prediction model establishment module is used for establishing a time sequence prediction model,
taking factors influencing the charging load of the electric vehicle as input of an electric vehicle charging load time sequence prediction model, and establishing the electric vehicle charging load time sequence prediction model based on a width learning system;
a timing prediction module configured to predict a timing of the first signal,
and inputting the factors influencing the charging load of the electric vehicle in the normalized data into an electric vehicle charging load time sequence prediction model to obtain a predicted value of the charging load of the electric vehicle.
4. A computer readable storage medium, characterized in that the computer readable storage medium stores a program code which, when executed by a processor, implements the steps of the electric vehicle charging load timing modeling method based on a width learning system as claimed in claim 1 or 2.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310099216.7A CN116187181B (en) | 2023-02-01 | 2023-02-01 | Electric vehicle charging load time sequence modeling method, system and medium based on width learning system |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310099216.7A CN116187181B (en) | 2023-02-01 | 2023-02-01 | Electric vehicle charging load time sequence modeling method, system and medium based on width learning system |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116187181A true CN116187181A (en) | 2023-05-30 |
| CN116187181B CN116187181B (en) | 2024-03-22 |
Family
ID=86439789
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310099216.7A Active CN116187181B (en) | 2023-02-01 | 2023-02-01 | Electric vehicle charging load time sequence modeling method, system and medium based on width learning system |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116187181B (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756638A (en) * | 2023-08-17 | 2023-09-15 | 广东电网有限责任公司汕头供电局 | Method, device, equipment and storage medium for detecting electric load demand of electric vehicle |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190265768A1 (en) * | 2018-02-24 | 2019-08-29 | Hefei University Of Technology | Method, system and storage medium for predicting power load probability density based on deep learning |
| CN114444821A (en) * | 2022-04-12 | 2022-05-06 | 国网湖北省电力有限公司电力科学研究院 | Integrated learning load prediction method, system and medium for power internet of things |
| CN114692956A (en) * | 2022-03-11 | 2022-07-01 | 广东电网有限责任公司广州供电局 | Charging facility load prediction method and system based on multilayer optimization kernel limit learning machine |
-
2023
- 2023-02-01 CN CN202310099216.7A patent/CN116187181B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20190265768A1 (en) * | 2018-02-24 | 2019-08-29 | Hefei University Of Technology | Method, system and storage medium for predicting power load probability density based on deep learning |
| CN114692956A (en) * | 2022-03-11 | 2022-07-01 | 广东电网有限责任公司广州供电局 | Charging facility load prediction method and system based on multilayer optimization kernel limit learning machine |
| CN114444821A (en) * | 2022-04-12 | 2022-05-06 | 国网湖北省电力有限公司电力科学研究院 | Integrated learning load prediction method, system and medium for power internet of things |
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116756638A (en) * | 2023-08-17 | 2023-09-15 | 广东电网有限责任公司汕头供电局 | Method, device, equipment and storage medium for detecting electric load demand of electric vehicle |
| CN116756638B (en) * | 2023-08-17 | 2023-11-14 | 广东电网有限责任公司汕头供电局 | Method, device, equipment and storage medium for detecting electric load demand of electric vehicle |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116187181B (en) | 2024-03-22 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Deb et al. | Charging station placement for electric vehicles: a case study of Guwahati city, India | |
| Shaheen et al. | Optimal parameter identification of linear and non-linear models for Li-Ion Battery Cells | |
| CN116187181B (en) | Electric vehicle charging load time sequence modeling method, system and medium based on width learning system | |
| CN110380444B (en) | Capacity planning method for distributed wind power orderly access to power grid under multiple scenes based on variable structure Copula | |
| CN112165084B (en) | Multi-time scale optimization method considering photovoltaic-load bilateral prediction uncertainty | |
| Zhang et al. | State‐of‐charge estimation of lithium‐ion battery pack based on improved RBF neural networks | |
| Hu et al. | Short-term photovoltaic power prediction based on similar days and improved SOA-DBN model | |
| CN105005825A (en) | Method and system for predicting photovoltaic power based on dynamic neural network | |
| CN114692956A (en) | Charging facility load prediction method and system based on multilayer optimization kernel limit learning machine | |
| CN116843083A (en) | Carbon emission prediction system and method based on hybrid neural network model | |
| Smith et al. | Electric vehicle charging via machine-learning pattern recognition | |
| Moon et al. | A comparative analysis of tree-based models for day-ahead solar irradiance forecasting | |
| CN112270454B (en) | Method and device for predicting short-term load of power system under influence of extreme factors | |
| Zhang et al. | Remaining useful life prediction of lithium-ion batteries based on TCN-DCN fusion model combined with IRRS filtering | |
| Lai et al. | Short‐term passenger flow prediction for rail transit based on improved particle swarm optimization algorithm | |
| Zhang et al. | Load forecasting method based on improved deep learning in cloud computing environment | |
| CN115267586A (en) | Lithium battery SOH evaluation method | |
| CN114444803A (en) | Method, system, terminal and medium for predicting charging load of electric vehicle charging station | |
| CN115764855A (en) | Real-time adjustable capacity and available electric quantity prediction method for electric vehicle quick charging station | |
| CN114881344A (en) | Training method, device and medium for building energy consumption prediction model | |
| Wu et al. | [Retracted] Application of Data Mining Algorithm in Electric Power Marketing Inspection Forecast Analysis | |
| Dong et al. | Image-based processing mechanism for peak load forecasting in smart grids | |
| CN111931994A (en) | Short-term load and photovoltaic power prediction method, system, equipment and medium thereof | |
| Su et al. | Combined-LSTM based user electricity consumption prediction in a smart grid system | |
| Chen et al. | Prediction method of intelligent building electricity consumption based on deep learning |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |