CN116960957A - Vehicle charging load prediction method and device, electronic equipment and storage medium - Google Patents
Vehicle charging load prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The invention discloses a vehicle charging load prediction method, a vehicle charging load prediction device, electronic equipment and a storage medium. The method comprises the following steps: acquiring original vehicle charging load data of an area to be evaluated; inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit; and inputting the charging load data of the repair vehicle into a charging load data prediction model to obtain the charging load data of the target vehicle. By the technical scheme, the repair and the prediction of the vehicle charging load data are realized, and the accuracy of the vehicle charging load data is improved.
Description
Technical Field
The present invention relates to the field of power distribution technologies, and in particular, to a method and apparatus for predicting a charging load of a vehicle, an electronic device, and a storage medium.
Background
With the popularization of electric vehicles, electric vehicles will bring important influence to the power system.
At present, due to the reasons of a terminal data collector or network communication faults and the like, the conditions of data missing, data abnormality and the like exist in vehicle charging load data.
In the process of implementing the present invention, the inventor finds that at least the following technical problems exist in the prior art: in the prior art, the problem of low accuracy of vehicle charging load data exists.
Disclosure of Invention
The invention provides a vehicle charging load prediction method, a vehicle charging load prediction device, electronic equipment and a storage medium, which are used for solving the problem of low accuracy of vehicle charging load data.
According to an aspect of the present invention, there is provided a vehicle charge load prediction method including:
acquiring original vehicle charging load data of an area to be evaluated;
inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit;
and inputting the charging load data of the repair vehicle into a charging load data prediction model to obtain the charging load data of the target vehicle.
According to another aspect of the present invention, there is provided a vehicle charge load prediction apparatus including:
The vehicle charging load data acquisition module is used for acquiring original vehicle charging load data of the area to be evaluated;
the vehicle charging load data interpolation module is used for inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulation unit;
and the vehicle charging load data prediction module is used for inputting the repairing vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
According to another aspect of the present invention, there is provided an electronic apparatus including:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle charging load prediction method according to any one of the embodiments of the present invention.
According to another aspect of the present invention, there is provided a computer readable storage medium storing computer instructions for causing a processor to execute the method for predicting a vehicle charging load according to any one of the embodiments of the present invention.
According to the technical scheme, the original vehicle charging load data of the area to be evaluated is obtained, the original vehicle charging load data of the area to be evaluated is further input into the charging load data interpolation model obtained based on the generation countermeasure network training comprising the gate control circulating unit, the repair of the vehicle charging load data is achieved, the repair vehicle charging load data is further input into the charging load data prediction model, accurate prediction of the vehicle charging load data is achieved, and accuracy of the vehicle charging load data is improved.
It should be understood that the description in this section is not intended to identify key or critical features of the embodiments of the invention or to delineate the scope of the invention. Other features of the present invention will become apparent from the description that follows.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings required for the description of the embodiments will be briefly described below, and it is apparent that the drawings in the following description are only some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flowchart of a vehicle charge load prediction method according to a first embodiment of the present invention;
fig. 2 is a flowchart of a vehicle charging load prediction method according to a second embodiment of the present invention;
fig. 3 is a flowchart of a vehicle charge load prediction method according to a third embodiment of the present invention;
fig. 4 is a flowchart of a vehicle charge load prediction method according to a fourth embodiment of the present invention;
FIG. 5 is a flow chart of a method for assessing admission capacity according to a fourth embodiment of the present invention;
fig. 6 is a schematic structural view of a vehicle charge load prediction apparatus according to a fifth embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device implementing a vehicle charge load prediction method according to an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It should be noted that the terms "first," "second," and the like in the description and the claims of the present invention and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Fig. 1 is a flowchart of a vehicle charging load prediction method according to a first embodiment of the present invention, where the method may be implemented by a vehicle charging load prediction device, and the vehicle charging load prediction device may be implemented in hardware and/or software, and the vehicle charging load prediction device may be configured in a terminal and/or a server. As shown in fig. 1, the method includes:
S110, acquiring original vehicle charging load data of the area to be evaluated.
In this embodiment, the area to be evaluated refers to a power distribution network area to be subjected to prediction of vehicle charging load data, for example, the area to be evaluated may be a residential area or an industrial production area within a preset range, where the vehicle may be an electric vehicle or a hybrid vehicle. The original vehicle charging load data refers to power data of a charging facility of the power distribution network for charging the vehicle in a preset time, and the power data is missing or abnormal data.
Specifically, the raw vehicle charging load data of the area to be evaluated may be obtained from a preset storage file of the electronic device, or may be obtained from other devices communicatively connected to the electronic device, which is not limited in any way.
S120, inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit.
In this embodiment, the charge load data interpolation model is a neural network model, which may be obtained based on generation of an countermeasure network training including a gating loop unit, where the gating loop unit may be a GRU unit (Gated Recurrent Unit) or a GRUI unit (Gated Recurrent Unit for data Imputation). Wherein the generating an countermeasure network includes a generator and a discriminator, and the gating loop unit may be provided in the generator and/or the discriminator, without limitation.
Specifically, the original vehicle charging load data of the area to be evaluated is used as input data of a charging load data interpolation model, and then the original vehicle charging load data of the area to be evaluated is input into the pre-trained charging load data interpolation model, so that the charging load data interpolation model outputs repairing vehicle charging load data, repairing of the vehicle charging load data is achieved, and accuracy of the vehicle charging load data is improved.
S130, inputting the repair vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
In this embodiment, the charge load data prediction model is an artificial intelligent model obtained by training in advance, and the network architecture thereof may be a cyclic neural network or a variant of the cyclic neural network, and the like, which is not limited herein.
Specifically, the repair vehicle charging load data can be used as input data of the charging load data prediction model, and then the repair vehicle charging load data is input into the pre-trained charging load data prediction model, so that the charging load data prediction model outputs target vehicle charging load data, the loss and abnormality of original vehicle charging load data are eliminated, the accurate prediction of the vehicle charging load data is realized, and the accuracy of the vehicle charging load data is improved.
In some alternative embodiments, after acquiring the raw vehicle charging load data for the area under evaluation, the method further comprises: performing outlier processing on the original vehicle charging load data of the area to be evaluated; and/or normalizing the original vehicle charging load data of the area to be evaluated.
The outlier processing method may be an absolute median deviation algorithm, a standard deviation algorithm, a percentile algorithm, or the like, which is not limited herein. By means of outlier processing of the original vehicle charging load data of the area to be evaluated, adverse effects caused by outliers can be eliminated, and accuracy of the vehicle charging load data is effectively improved. The vehicle charging load data is limited in a preset range by carrying out normalization processing on the original vehicle charging load data of the area to be evaluated, so that adverse effects caused by singular sample data are eliminated.
According to the technical scheme, the original vehicle charging load data of the area to be evaluated is obtained, the original vehicle charging load data of the area to be evaluated is input into the charging load data interpolation model, the repair of the vehicle charging load data is achieved, the repair vehicle charging load data is input into the charging load data prediction model, the accurate prediction of the vehicle charging load data is achieved, and the accuracy of the vehicle charging load data is improved.
Example two
Fig. 2 is a flowchart of a vehicle charging load prediction method according to a second embodiment of the present invention, where the method according to the present embodiment may be combined with each of the alternatives in the vehicle charging load prediction method provided in the foregoing embodiment. The vehicle charging load prediction method provided by the embodiment is further optimized. Optionally, the training step of the charge load data interpolation model includes: acquiring vehicle charging load sample data; adding noise to the vehicle charging load sample data to obtain vehicle charging load generation data; and performing game countermeasure training on the generated countermeasure network comprising the gating circulating unit based on the vehicle charging load sample data and the vehicle charging load generation data to obtain a charging load data interpolation model.
As shown in fig. 2, the method includes:
s210, acquiring vehicle charging load sample data.
In this embodiment, the vehicle charge load sample data refers to sample data for training the charge load data interpolation model, which may include a plurality of vehicle charge load data.
Specifically, the vehicle charging load sample data may be obtained from a preset storage file of the electronic device, or may be obtained from another device that is communicatively connected to the electronic device, which is not limited in any way.
And S220, adding noise to the vehicle charging load sample data to obtain vehicle charging load generation data.
In order to improve the stability of the generator in the generation countermeasure network, random noise is introduced to obtain corrupted vehicle charging load generation data.
And S230, performing game countermeasure training on the generated countermeasure network comprising the gate control circulating unit based on the vehicle charging load sample data and the vehicle charging load generation data to obtain a charging load data interpolation model.
Optionally, based on the vehicle charging load sample data and the vehicle charging load generation data, performing game countermeasure training on a generated countermeasure network including a gate control circulation unit to obtain a charging load data interpolation model, including: determining a discriminator return loss corresponding to the vehicle charge load sample data, and determining a loss expectation corresponding to the vehicle charge load sample data based on the discriminator return loss corresponding to the vehicle charge load sample data; determining a discriminator return loss corresponding to the vehicle charge load generation data, and determining a loss expectation corresponding to the vehicle charge load generation data based on the discriminator return loss corresponding to the vehicle charge load generation data; and performing game countermeasure training on the generated countermeasure network comprising the gating circulating unit based on the loss expectation corresponding to the vehicle charging load sample data and the loss expectation corresponding to the vehicle charging load generation data to obtain a charging load data interpolation model.
Illustratively, the game challenge training function is:
wherein min is gen max dis V (gen, dis) is a min-max game countermeasure training function; x represents vehicle charge load sample data, DIS (x) represents discriminator return loss corresponding to the vehicle charge load sample data,indicating loss expectations corresponding to vehicle charge load sample data, x 1 DIS (x) 1 ) Identifier return loss indicating correspondence of vehicle charge load generation data,/->Indicating a loss expectation corresponding to vehicle charge load generation data, P x (x) A probability distribution representing vehicle charge load sample data; />A probability distribution representing vehicle charge load generation data.
S240, acquiring original vehicle charging load data of the area to be evaluated.
S250, inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit.
S260, inputting the repair vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
In some alternative embodiments, in order to reduce the error of the vehicle charge load generation data generated by the generator and the vehicle charge load sample data, a loss function is introduced in the generator, the loss function being specifically:
wherein β represents a coefficient for adjusting the loss function; l (L) gen Representing a loss function of the discriminator; l (L) dis Representing a loss function of the generator; s represents a position matrix of vehicle charging load sample data, wherein the position matrix consists of 0 or 1, 0 is a data position needing no interpolation, and 1 is a data position needing interpolation;representing element-wise multiplication, G (x+α) is a generator function, α representing random noise.
According to the technical scheme, the vehicle charging load sample data are obtained, noise is added to the vehicle charging load sample data, vehicle charging load generation data are obtained, game countermeasure training is carried out on a generated countermeasure network comprising a gate control circulation unit based on the vehicle charging load sample data and the vehicle charging load generation data, a charging load data interpolation model is obtained, training of the charging load data interpolation model is achieved, and data restoration accuracy of the charging load data interpolation model is improved.
Example III
Fig. 3 is a flowchart of a vehicle charging load prediction method according to a third embodiment of the present invention, where the method according to the present embodiment may be combined with each of the alternatives in the vehicle charging load prediction method provided in the foregoing embodiment. The vehicle charging load prediction method provided by the embodiment is further optimized. Optionally, the training step of the charging load data prediction model includes: acquiring repair vehicle charging load sample data and tag data corresponding to the repair vehicle charging load sample data; inputting the repair vehicle charging load sample data into an initial charging load data prediction model to obtain predicted vehicle charging load data; performing inverse normalization processing on the predicted vehicle charging load data to obtain predicted vehicle charging load data after inverse normalization processing; determining model loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data, updating parameters of an initial charging load data prediction model based on the model loss until the initial charging load data prediction model converges, and stopping training to obtain the charging load data prediction model.
As shown in fig. 3, the method includes:
s310, acquiring repair vehicle charging load sample data and tag data corresponding to the repair vehicle charging load sample data.
S320, inputting the repair vehicle charging load sample data into an initial charging load data prediction model to obtain predicted vehicle charging load data.
S330, performing inverse normalization processing on the predicted vehicle charging load data to obtain predicted vehicle charging load data after inverse normalization processing.
In this embodiment, the inverse normalization process formula is expressed as follows:
y pre,m ＝y pre (x n-max -x n-min )+x n-min ；
wherein y is pre,m Predicted vehicle charge load data after the inverse normalization processing at the mth time; y is pre Predicted vehicle charge load data indicating the mth time; x is x n-max Representing the maximum value, x, in the repair vehicle charge load sample data n-min Representing the minimum value of the repair vehicle charge load sample data.
And S340, determining model loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data, updating parameters of the initial charging load data prediction model based on the model loss until the initial charging load data prediction model converges, and stopping training to obtain the charging load data prediction model.
S350, acquiring original vehicle charging load data of the area to be evaluated.
S360, inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit.
And S370, inputting the repair vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
Optionally, determining the model loss based on the tag data corresponding to the predicted vehicle charging load data and the repair vehicle charging load sample data after the inverse normalization processing includes: determining average absolute percentage error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data; determining a mean square error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data; determining model accuracy loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data; the model loss is determined based on the mean absolute percentage error loss, the mean square error loss, and the model accuracy loss.
The mean absolute percentage error loss is calculated by the following formula:
wherein y is m Tag data corresponding to repair vehicle charge load sample data indicating the mth time, y pre,m Predicted vehicle charge load data after the inverse normalization processing at the mth time; m represents the total number of prediction results. The average absolute percentage error loss may evaluate the good prediction result of the charge load data prediction model, and the smaller the average absolute percentage error loss, the more accurate the prediction result of the charge load data prediction model.
The mean square error loss calculation formula is as follows:
the smaller the mean square error loss is, the smaller the prediction result error of the charging load data prediction model is.
The calculation formula of the model accuracy loss is as follows:
wherein, the larger the model accuracy loss is, the higher the accuracy of the charging load data prediction model prediction is.
According to the technical scheme, the original vehicle charging load data of the area to be evaluated is obtained, the original vehicle charging load data of the area to be evaluated is input into the charging load data interpolation model, the repair of the vehicle charging load data is achieved, the repair vehicle charging load data is input into the charging load data prediction model, the accurate prediction of the vehicle charging load data is achieved, and the accuracy of the vehicle charging load data is improved.
Example IV
Fig. 4 is a flowchart of a vehicle charging load prediction method according to a fourth embodiment of the present invention, where the method according to the present embodiment may be combined with each of the alternatives in the vehicle charging load prediction method provided in the foregoing embodiment. The vehicle charging load prediction method provided by the embodiment is further optimized. Optionally, after the repairing vehicle charging load data is input to the charging load data prediction model to obtain target vehicle charging load data, the method further includes: acquiring an objective function of vehicle charging load admission capacity evaluation and constraint conditions corresponding to the objective function of the vehicle charging load admission capacity evaluation; and under the constraint condition corresponding to the objective function of the vehicle charging load acceptance capability assessment, solving the objective function of the vehicle charging load acceptance capability assessment based on the objective vehicle charging load data to obtain an objective acceptance capability assessment parameter.
As shown in fig. 4, the method includes:
s410, acquiring original vehicle charging load data of the area to be evaluated.
S420, inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit.
S430, inputting the repair vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
S440, obtaining a constraint condition corresponding to an objective function of vehicle charge load admission capacity evaluation.
S450, under the constraint condition corresponding to the objective function of the vehicle charging load acceptance capability assessment, solving the objective function of the vehicle charging load acceptance capability assessment based on the objective vehicle charging load data to obtain an objective acceptance capability assessment parameter.
In this embodiment, the objective function of the vehicle charge load acceptance capability evaluation may be:
wherein F represents an objective function; e (c) 1 f 1 +c 2 f 2 +c 3 f 3 ) Representing expected values of a power loss function, a voltage deviation function, and a voltage margin function; f (f) 1 、f 2 、f 3 Respectively representing a power loss function, a voltage deviation function and a voltage margin function; c 1 、c 2 、c 3 Is a weight coefficient; p (P) j And Q j Respectively representing the active power and the reactive power of the node j; u (U) j And U N Respectively representing the voltage and rated voltage of the node j; m and n respectively represent the sum of the branch numbersNode number; l (L) ij A voltage stability margin index between the node i and the node j; r is R ij Representing the resistance value between node i and node j; x is X ij Representing the reactance between node i and node j.
The constraint conditions comprise power distribution network system capacity constraint, node voltage constraint, active power constraint and power flow balance constraint, and specifically comprise:
wherein P is EV Representing target vehicle charge load data; p (P) normal Representing conventional electricity load data of residential areas; c (C) max Representing the maximum capacity of the power distribution network system; u (U) imin And U imax Respectively representing a lower voltage limit and an upper voltage limit of the node i; p (P) i Representing the active power of the i-th node; q (Q) i Representing reactive power of the i-th node; u (U) i And U j Respectively representing i node voltage and j node voltage; n represents the number of nodes; g ij And B ij Respectively representing a real part and an imaginary part of the node admittance matrix; delta ij Representing the i and j node phase angle differences.
Fig. 5 is a flowchart of an admission capacity evaluation method according to an embodiment of the present invention. Specifically, the original vehicle charging load data is obtained, and further, outlier processing, normalization and other preprocessing operations can be performed on the original vehicle charging load data, wherein the outlier processing can be a four-bit distance outlier processing method and the like, and further, the preprocessed original vehicle charging load data is input into a charging load data interpolation model based on a GRUI-GAN (Gated Recurrent Unit for data Imputation-Recurrent Neural Network) neural network to obtain repair vehicle charging load data, and further, the repair vehicle charging load data is input into a charging load data prediction model based on an LTC-RNN (liquid time constant-recurrent neural network) neural network to obtain target vehicle charging load data; and then constructing a vehicle charging load admission capacity evaluation model, wherein the vehicle charging load admission capacity evaluation model comprises an objective function and constraint conditions, and then importing target vehicle charging load data into the vehicle charging load admission capacity evaluation model, solving to obtain target admission capacity evaluation parameters, realizing the evaluation of the vehicle charging load admission capacity, and providing a basis for vehicle charging facility planning.
According to the technical scheme provided by the embodiment of the invention, the objective function of the vehicle charge load admission capacity assessment is solved based on the objective vehicle charge load data under the constraint condition corresponding to the objective function of the vehicle charge load admission capacity assessment, so that the objective admission capacity assessment parameters are obtained, the accurate assessment of the objective admission capacity is realized, and a basis is provided for vehicle charge facility planning.
Example five
Fig. 6 is a schematic structural diagram of a vehicle charging load prediction apparatus according to a fifth embodiment of the present invention. As shown in fig. 6, the apparatus includes:
the vehicle charging load data acquisition module 510 is configured to acquire original vehicle charging load data of an area to be evaluated;
the vehicle charging load data interpolation module 520 is configured to input original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain repair vehicle charging load data, where the charging load data interpolation model is obtained based on generation countermeasure network training including a gating cycle unit;
the vehicle charging load data prediction module 530 is configured to input the repair vehicle charging load data into a charging load data prediction model, and obtain target vehicle charging load data.
According to the technical scheme, the original vehicle charging load data of the area to be evaluated is obtained, the original vehicle charging load data of the area to be evaluated is input into the charging load data interpolation model, the repair of the vehicle charging load data is achieved, the repair vehicle charging load data is input into the charging load data prediction model, the accurate prediction of the vehicle charging load data is achieved, and the accuracy of the vehicle charging load data is improved.
In some alternative embodiments, a vehicle charge load prediction apparatus includes:
the vehicle charging load sample data module is used for acquiring vehicle charging load sample data;
the sample data adding noise module is used for adding noise to the vehicle charging load sample data to obtain vehicle charging load generation data;
and the game countermeasure training module is used for performing game countermeasure training on the generated countermeasure network comprising the gating circulation unit based on the vehicle charging load sample data and the vehicle charging load generation data to obtain a charging load data interpolation model.
In some alternative embodiments, the gaming countermeasure training module is specifically configured to:
Determining a discriminator return loss corresponding to the vehicle charge load sample data, and determining a loss expectation corresponding to the vehicle charge load sample data based on the discriminator return loss corresponding to the vehicle charge load sample data;
determining a discriminator return loss corresponding to the vehicle charge load generation data, and determining a loss expectation corresponding to the vehicle charge load generation data based on the discriminator return loss corresponding to the vehicle charge load generation data;
and performing game countermeasure training on the generated countermeasure network comprising the gating circulating unit based on the loss expectation corresponding to the vehicle charging load sample data and the loss expectation corresponding to the vehicle charging load generation data to obtain a charging load data interpolation model.
In some alternative embodiments, a vehicle charge load prediction apparatus includes:
the repair vehicle load sample data acquisition module is used for acquiring repair vehicle charging load sample data and tag data corresponding to the repair vehicle charging load sample data;
the vehicle charging load data prediction module is used for inputting the repair vehicle charging load sample data into an initial charging load data prediction model to obtain predicted vehicle charging load data;
The vehicle charging load data inverse normalization module is used for performing inverse normalization processing on the predicted vehicle charging load data to obtain predicted vehicle charging load data after the inverse normalization processing;
and the prediction model training module is used for determining model loss based on the inverse normalization processed predicted vehicle charging load data and the label data corresponding to the repair vehicle charging load sample data, updating parameters of the initial charging load data prediction model based on the model loss until the initial charging load data prediction model converges, and stopping training to obtain the charging load data prediction model.
In some alternative embodiments, the predictive model training module is further configured to:
determining average absolute percentage error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
determining a mean square error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
determining model accuracy loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
Determining a model loss based on the mean absolute percentage error loss, the mean square error loss, and the model accuracy loss.
In some alternative embodiments, the vehicle charge load prediction apparatus further includes:
the data preprocessing module is used for performing outlier processing on the original vehicle charging load data of the region to be evaluated; and/or normalizing the original vehicle charging load data of the region to be evaluated.
In some alternative embodiments, the vehicle charge load prediction apparatus further includes:
the system comprises an objective function acquisition module, a constraint condition acquisition module and a constraint condition analysis module, wherein the objective function acquisition module is used for acquiring an objective function of vehicle charging load admittance capability evaluation and a constraint condition corresponding to the objective function of the vehicle charging load admittance capability evaluation;
and the objective function solving module is used for solving the objective function of the vehicle charge load admission capacity evaluation based on the objective vehicle charge load data under the constraint condition corresponding to the objective function of the vehicle charge load admission capacity evaluation to obtain an objective admission capacity evaluation parameter.
The vehicle charging load prediction device provided by the embodiment of the invention can execute the vehicle charging load prediction method provided by any embodiment of the invention, and has the corresponding functional modules and beneficial effects of the execution method.
Example six
Fig. 7 shows a schematic diagram of the structure of an electronic device 10 that may be used to implement an embodiment of the invention. Electronic devices are intended to represent various forms of digital computers, such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computers. Electronic equipment may also represent various forms of mobile devices, such as personal digital assistants, cellular telephones, smartphones, wearable devices (e.g., helmets, eyeglasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions, are meant to be exemplary only, and are not meant to limit implementations of the inventions described and/or claimed herein.
As shown in fig. 7, the electronic device 10 includes at least one processor 11, and a memory, such as a Read Only Memory (ROM) 12, a Random Access Memory (RAM) 13, etc., communicatively connected to the at least one processor 11, in which the memory stores a computer program executable by the at least one processor, and the processor 11 may perform various appropriate actions and processes according to the computer program stored in the Read Only Memory (ROM) 12 or the computer program loaded from the storage unit 18 into the Random Access Memory (RAM) 13. In the RAM 13, various programs and data required for the operation of the electronic device 10 may also be stored. The processor 11, the ROM 12 and the RAM 13 are connected to each other via a bus 14. An I/O interface 15 is also connected to bus 14.
Various components in the electronic device 10 are connected to the I/O interface 15, including: an input unit 16 such as a keyboard, a mouse, etc.; an output unit 17 such as various types of displays, speakers, and the like; a storage unit 18 such as a magnetic disk, an optical disk, or the like; and a communication unit 19 such as a network card, modem, wireless communication transceiver, etc. The communication unit 19 allows the electronic device 10 to exchange information/data with other devices via a computer network, such as the internet, and/or various telecommunication networks.
The processor 11 may be a variety of general and/or special purpose processing components having processing and computing capabilities. Some examples of processor 11 include, but are not limited to, a Central Processing Unit (CPU), a Graphics Processing Unit (GPU), various specialized Artificial Intelligence (AI) computing chips, various processors running machine learning model algorithms, digital Signal Processors (DSPs), and any suitable processor, controller, microcontroller, etc. The processor 11 performs the various methods and processes described above, such as a vehicle charge load prediction method, which includes:
acquiring original vehicle charging load data of an area to be evaluated;
inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit;
And inputting the charging load data of the repair vehicle into a charging load data prediction model to obtain the charging load data of the target vehicle.
In some embodiments, the vehicle charge load prediction method may be implemented as a computer program tangibly embodied on a computer-readable storage medium, such as the storage unit 18. In some embodiments, part or all of the computer program may be loaded and/or installed onto the electronic device 10 via the ROM 12 and/or the communication unit 19. When the computer program is loaded into RAM 13 and executed by processor 11, one or more steps of the vehicle charge load prediction method described above may be performed. Alternatively, in other embodiments, the processor 11 may be configured to perform the vehicle charge load prediction method in any other suitable manner (e.g., by means of firmware).
Various implementations of the systems and techniques described here above may be implemented in digital electronic circuitry, integrated circuit systems, field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems On Chip (SOCs), complex Programmable Logic Devices (CPLDs), computer hardware, firmware, software, and/or combinations thereof. These various embodiments may include: implemented in one or more computer programs, the one or more computer programs may be executed and/or interpreted on a programmable system including at least one programmable processor, which may be a special purpose or general-purpose programmable processor, that may receive data and instructions from, and transmit data and instructions to, a storage system, at least one input device, and at least one output device.
A computer program for carrying out methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus, such that the computer programs, when executed by the processor, cause the functions/acts specified in the flowchart and/or block diagram block or blocks to be implemented. The computer program may execute entirely on the machine, partly on the machine, as a stand-alone software package, partly on the machine and partly on a remote machine or entirely on the remote machine or server.
In the context of the present invention, a computer-readable storage medium may be a tangible medium that can contain, or store a computer program for use by or in connection with an instruction execution system, apparatus, or device. The computer readable storage medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. Alternatively, the computer readable storage medium may be a machine readable signal medium. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, 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 disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
To provide for interaction with a user, the systems and techniques described here can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to a user; and a keyboard and a pointing device (e.g., a mouse or a trackball) through which a user can provide input to the electronic device. Other kinds of devices may also be used to provide for interaction with a user; for example, feedback provided to the user may be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user may be received in any form, including acoustic input, speech input, or tactile input.
The systems and techniques described here can be implemented in a computing system that includes a background component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front-end component (e.g., a user computer having a graphical user interface or a web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such background, middleware, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: local Area Networks (LANs), wide Area Networks (WANs), blockchain networks, and the internet.
The computing system may include clients and servers. The client and server are typically remote from each other and typically interact through a communication network. The relationship of client and server arises by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can be a cloud server, also called a cloud computing server or a cloud host, and is a host product in a cloud computing service system, so that the defects of high management difficulty and weak service expansibility in the traditional physical hosts and VPS service are overcome.
It should be appreciated that various forms of the flows shown above may be used to reorder, add, or delete steps. For example, the steps described in the present invention may be performed in parallel, sequentially, or in a different order, so long as the desired results of the technical solution of the present invention are achieved, and the present invention is not limited herein.
The above embodiments do not limit the scope of the present invention. It will be apparent to those skilled in the art that various modifications, combinations, sub-combinations and alternatives are possible, depending on design requirements and other factors. Any modifications, equivalent substitutions and improvements made within the spirit and principles of the present invention should be included in the scope of the present invention.
Claims (10)
1. A vehicle charge load prediction method, characterized by comprising:
acquiring original vehicle charging load data of an area to be evaluated;
inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulating unit;
and inputting the charging load data of the repair vehicle into a charging load data prediction model to obtain the charging load data of the target vehicle.
2. The method of claim 1, wherein the training step of the charge load data interpolation model comprises:
acquiring vehicle charging load sample data;
adding noise to the vehicle charging load sample data to obtain vehicle charging load generation data;
and performing game countermeasure training on the generated countermeasure network comprising the gating circulating unit based on the vehicle charging load sample data and the vehicle charging load generation data to obtain a charging load data interpolation model.
3. The method of claim 2, wherein the game countermeasure training is performed on the generated countermeasure network including the gate cycle unit based on the vehicle charging load sample data and the vehicle charging load generation data to obtain a charging load data interpolation model, comprising:
Determining a discriminator return loss corresponding to the vehicle charge load sample data, and determining a loss expectation corresponding to the vehicle charge load sample data based on the discriminator return loss corresponding to the vehicle charge load sample data;
determining a discriminator return loss corresponding to the vehicle charge load generation data, and determining a loss expectation corresponding to the vehicle charge load generation data based on the discriminator return loss corresponding to the vehicle charge load generation data;
and performing game countermeasure training on the generated countermeasure network comprising the gating circulating unit based on the loss expectation corresponding to the vehicle charging load sample data and the loss expectation corresponding to the vehicle charging load generation data to obtain a charging load data interpolation model.
4. The method of claim 1, wherein the training of the charge load data prediction model comprises:
acquiring repair vehicle charging load sample data and tag data corresponding to the repair vehicle charging load sample data;
inputting the repair vehicle charging load sample data into an initial charging load data prediction model to obtain predicted vehicle charging load data;
Performing inverse normalization processing on the predicted vehicle charging load data to obtain predicted vehicle charging load data after inverse normalization processing;
determining model loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data, updating parameters of an initial charging load data prediction model based on the model loss until the initial charging load data prediction model converges, and stopping training to obtain the charging load data prediction model.
5. The method of claim 4, wherein the determining a model loss based on the inverse normalized predicted vehicle charge load data and the tag data corresponding to the repair vehicle charge load sample data comprises:
determining average absolute percentage error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
determining a mean square error loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
Determining model accuracy loss based on the predicted vehicle charging load data after the inverse normalization processing and the label data corresponding to the repair vehicle charging load sample data;
determining a model loss based on the mean absolute percentage error loss, the mean square error loss, and the model accuracy loss.
6. The method according to any one of claims 1-5, characterized in that after the acquisition of the raw vehicle charging load data of the area to be evaluated, the method further comprises:
performing outlier processing on the original vehicle charging load data of the region to be evaluated;
and/or normalizing the original vehicle charging load data of the region to be evaluated.
7. The method of any one of claims 1-5, wherein after the inputting the repair vehicle charge load data into a charge load data prediction model to obtain target vehicle charge load data, the method further comprises:
acquiring an objective function of vehicle charging load admission capacity evaluation and constraint conditions corresponding to the objective function of the vehicle charging load admission capacity evaluation;
and under the constraint condition corresponding to the objective function of the vehicle charging load acceptance capability assessment, solving the objective function of the vehicle charging load acceptance capability assessment based on the objective vehicle charging load data to obtain an objective acceptance capability assessment parameter.
8. A vehicle charge load prediction apparatus, comprising:
the vehicle charging load data acquisition module is used for acquiring original vehicle charging load data of the area to be evaluated;
the vehicle charging load data interpolation module is used for inputting the original vehicle charging load data of the region to be evaluated into a charging load data interpolation model to obtain the repairing vehicle charging load data, wherein the charging load data interpolation model is obtained based on generation countermeasure network training comprising a gating circulation unit;
and the vehicle charging load data prediction module is used for inputting the repairing vehicle charging load data into a charging load data prediction model to obtain target vehicle charging load data.
9. An electronic device, the electronic device comprising:
at least one processor;
and a memory communicatively coupled to the at least one processor;
wherein the memory stores a computer program executable by the at least one processor to enable the at least one processor to perform the vehicle charging load prediction method of any one of claims 1-7.
10. A computer readable storage medium storing computer instructions for causing a processor to implement the vehicle charging load prediction method of any one of claims 1-7 when executed.
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