A new energy market clearing sample set batch generation method and system

Abstract

The application discloses a new energy market clearing sample set batch generation method and system, and belongs to the technical field of electric power data processing. The method comprises the following steps: taking the power generation capacity of a nearby power plant as source domain data and the power generation capacity of a newly-built power plant as target domain data, inputting the source domain data and the target domain data into a generator and a discriminator respectively, extracting time sequence features of the power generation data to obtain source domain features and target domain features respectively, mapping the source domain features and the target domain features to a manifold domain through a Grassmann manifold kernel function, calculating the geodesic distance between the source domain and the target domain in the manifold domain, taking the geodesic distance as a loss function of a sample set generation model, and generating a new energy market clearing sample set in batches by using the sample set generation model corresponding to the loss function. The method can predict the photovoltaic power generation capacity of all power supply areas through the power curve data of a single user.

Description

Technical Field

[0001] This application proposes a method and system for batch generation of new energy market clearing sample sets, relating to the field of power data processing technology. Background Art

[0002] With the rapid development of renewable energy installations, renewable energy output will affect the supply and demand relationship in the overall electricity market. However, there are quality issues such as missing, duplication, and anomalies in the collection and transmission of power data, and poor data sample quality will affect the price mechanism of market clearing. Therefore, sample set generation methods play an important role in power market clearing, power market transactions, and economic dispatch. Constructing higher-quality sample sets can make the stochastic optimization problem of the power system closer to the optimal solution. However, in the early stages of operation of renewable energy power plants, the data may be very limited or there may be no historical information, which will lead to inaccurate sample set generation and thus affect the reliability of decision-making results. Considering that multiple power plants in neighboring areas may have similar weather conditions, it is possible to consider using other existing data-rich historical information of power plants to generate samples.

[0003] Using nearby fields to generate sample sets presents the following challenges: 1. New energy samples may be affected by various climate phenomena. The feature spaces of data from geographically close power plants may differ, but they may also share a certain degree of similarity. Existing sample set generation methods generate data in the original feature space, with power generation data for newly built power plants and nearby power plants in different feature spaces. 2. Because features in the original space are often distorted, this can cause data drift between the source and target domains, leading to underfitting of learning results. Summary of the Invention

[0004] In order to solve the problems existing in the prior art, this application proposes a method and system for batch generation of new energy market clearing sample sets, which can predict the photovoltaic power generation of the entire area based on the power curve data of a single user.

[0005] In order to solve the above technical problems, the technical solutions adopted in this application are:

[0006] In a first aspect, the present application provides a method for batch generating a new energy market clearing sample set, comprising:

[0007] The power generation of the adjacent electric field is used as the source domain data, and the power generation of the newly built electric field is used as the target domain data. The source domain data and target domain data are input into the generator and discriminator respectively, and the time series features of the power generation data are extracted to obtain the source domain features and target domain features respectively.

[0008] Through the Grassmann manifold kernel function, the source domain features and target domain features are mapped to the manifold domain;

[0009] Calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model;

[0010] The sample set generation model corresponding to the loss function is used to batch generate new energy market clearing sample sets.

[0011] As a further improvement of the present invention, the generator and discriminator are time-domain convolutional networks (TCNs).

[0012] As a further improvement of the present invention, the time series features of the power generation data are extracted to obtain source domain features and target domain features, which specifically include:

[0013] Power generation data of the source domain , target domain power generation data , for the input sequence , causal convolution is expressed as:

[0014]

[0015] in, is the convolution kernel, is the convolution kernel size, yes The input sequence of moments;

[0016] Then the dilated convolution of TCN is expressed as:

[0017]

[0018] in, is the expansion rate;

[0019] The time series features of power generation data are extracted through TCN to obtain source domain features. and target domain features .

[0020] As a further improvement of the present invention, mapping the source domain features and the target domain features to the manifold domain by using the Grassmann manifold kernel function includes:

[0021] Map the source domain features and target domain features into the manifold domain; for the source domain features and target domain features , in the manifold domain Calculate the corresponding geodesic ;

[0022] The geodesic flow kernel function GFK is used to map the features in the original space to the manifold domain; the transformed features and The inner product of defines a semi-positive definite geodesic flow kernel, and the GFK kernel function is defined as follows:

[0023]

[0024] The features in the original space are obtained by Transformed to the Grassmann manifold, It is a form of expression.

[0025] As a further improvement of the present invention, the step of calculating the geodesic distance between the source domain and the target domain in the manifold domain and using the geodesic distance as a loss function of the sample set generation model includes:

[0026] For source domain features mapped to manifold domain and target domain features , optimize source domain features and target domain features Based on the loss function of the manifold geodesic distance, the KL divergence of the source domain features and the target domain features on the manifold is calculated, and the loss function is as follows:

[0027]

[0028] Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

[0029] As a further improvement of the present invention, the batch generation of new energy market clearing sample sets using the sample set generation model corresponding to the loss function includes:

[0030] The target domain data is input into a sample set generation model corresponding to the loss function to generate new energy market clearing sample sets in batches.

[0031] In a second aspect, the present application provides a system for batch generation of new energy market clearing sample sets, comprising:

[0032] The extraction module is used to use the power generation of the adjacent electric field as the source domain data and the power generation of the newly built electric field as the target domain data. The source domain data and target domain data are input into the generator and discriminator respectively, and the time series features of the power generation data are extracted to obtain the source domain features and target domain features respectively.

[0033] A mapping module is used to map the source domain features and the target domain features to the manifold domain through the Grassmann manifold kernel function;

[0034] A calculation module is used to calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model;

[0035] A generation module is used to generate batches of new energy market clearing sample sets using a sample set generation model corresponding to the loss function.

[0036] Further preferably, the extraction module is specifically used to:

[0037] Power generation data of the source domain , target domain power generation data , for the input sequence , causal convolution is expressed as:

[0038]

[0039] in, is the convolution kernel, is the convolution kernel size, yes The input sequence of moments;

[0040] Then the dilated convolution of TCN is expressed as:

[0041]

[0042] in, is the expansion rate;

[0043] The time series features of power generation data are extracted through TCN to obtain source domain features. and target domain features .

[0044] Further preferably, the mapping module is specifically used to:

[0045] Map the source domain features and target domain features into the manifold domain; for the source domain features and target domain features , in the manifold domain Calculate the corresponding geodesic ;

[0046] The geodesic flow kernel function GFK is used to map the features in the original space to the manifold domain; the transformed features and The inner product of defines a semi-positive definite geodesic flow kernel, and the GFK kernel function is defined as follows:

[0047]

[0048] The features in the original space are obtained by Transformed to the Grassmann manifold, It is a form of expression.

[0049] Further preferably, the calculation module is specifically used to:

[0050] For source domain features mapped to manifold domain and target domain features , optimize source domain features and target domain features Based on the loss function of the manifold geodesic distance, the KL divergence of the source domain features and the target domain features on the manifold is calculated, and the loss function is as follows:

[0051]

[0052] Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

[0053] Further preferably, the generating module is specifically used to:

[0054] The target domain data is input into a sample set generation model corresponding to the loss function to generate new energy market clearing sample sets in batches.

[0055] In a third aspect, the present application provides an electronic device comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the method for batch generating a new energy market clearing sample set is implemented.

[0056] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the method for batch generating new energy market clearing sample sets.

[0057] In a fifth aspect, the present application provides a computer program product, which includes computer instructions, and the computer instructions instruct a computer to execute the method for batch generating new energy market clearing sample sets.

[0058] The beneficial effects of this application compared to the prior art are:

[0059] This application proposes a method for batch generation of sample sets based on domain migration, which uses a transfer learning method to learn the data distribution between the newly built electric field and the adjacent electric field. A large amount of source domain data is used to train the generator model, while a small amount of target domain data is used to train the discriminator model. Finally, the distribution difference between the generator and discriminator outputs is optimized. Since the power generation data is a time series, the feature extraction modules of the generator and discriminator in this application use TCN (Temporal Convolutional Network, time domain convolutional network), and TCN uses causal convolution to learn time series features. Specifically, the adjacent electric field (source domain) data is first input into the generator model, and the newly built electric field (target domain) data is input into the discriminator model, and the KL divergence between the generator and discriminator outputs is optimized.

[0060] Specifically, this application proposes a manifold-based feature transformation method. According to the manifold hypothesis, points embedded in the manifold space and their neighbors usually have similar properties. Therefore, this application proposes a manifold-based domain transfer learning method, which performs feature transformation based on the manifold space to reduce data drift between domains. The features are mapped to the manifold space through the Grassmann kernel function, and the geodesic function between the source domain and the target domain is calculated using the geodesic flow kernel. The geodesic distance between the source domain and the target domain is optimized by the KL divergence, so that the data generated using the source domain data is close to the target domain data in distribution. BRIEF DESCRIPTION OF THE DRAWINGS

[0061] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the following introduction is made to the drawings of the embodiments of the present application or the related technical solutions in the prior art. It should be understood that the drawings introduced below are only for the convenience of clearly describing some embodiments of the technical solutions of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without any creative work.

[0062] Figure 1 A flow chart of a method for batch generation of new energy market clearing sample sets provided in this application;

[0063] Figure 2 This is a flow chart of a method for batch generation of new energy market clearing sample sets based on manifold domain migration in an embodiment of the present application;

[0064] Figure 3 Schematic diagram of the TCN model provided in the embodiment of the present application;

[0065] Figure 4 A schematic diagram showing the visualization of geodesic distance provided in an embodiment of the present application;

[0066] Figure 5A schematic diagram showing the visualization of the manifold domain data distribution provided in the embodiment of the present application;

[0067] Figure 6 A device for batch generation of new energy market clearing sample sets provided in this application;

[0068] Figure 7 A schematic diagram of an electronic device provided in this application. DETAILED DESCRIPTION

[0069] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application and are not to be construed as limiting the present application. For the step numbers in the following embodiments, they are provided only for the convenience of explanation and are not intended to limit the order of the steps. The order of execution of the steps in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0070] In the description of this application, unless otherwise clearly defined, terms such as setting, installing, and connecting should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meanings of the above terms in this application based on the specific content of the technical solution.

[0071] This application addresses the issue of insufficient newly constructed electric field samples by using transfer learning to learn target domain (newly constructed electric field) data from adjacent electric fields as source domain data. To address the distortion of the original feature space, a method for batch generation of new energy sample sets based on manifold domain transfer is proposed. This approach leverages the topological invariance of manifold learning to address the issue of data drift between domains.

[0072] Specifically, if Figure 1 As shown, the first purpose of this application is to provide a method for batch generating a new energy market clearing sample set, including:

[0073] S101. The power generation of the adjacent electric field is used as the source domain data, and the power generation of the newly created electric field is used as the target domain data. The source domain data and the target domain data are input into the generator and the discriminator respectively to obtain the time series characteristics of the power generation data.

[0074] S102. Map the source domain features and the target domain features to the manifold domain through the Grassmann manifold kernel function.

[0075] S103. Calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model.

[0076] S104: Batch generate new energy market clearing sample sets using the sample set generation model corresponding to the loss function.

[0077] This application addresses the problem of insufficient samples of newly built electric fields. By applying the idea of ​​transfer learning and combining it with the characteristics of manifold learning, a method for batch generation of clearing sample sets for the new energy market is proposed. In the transfer learning application, the source domain data selection is to select the power generation data of nearby electric fields as the source domain data. These electric fields are similar to the newly built electric fields in terms of geographical location, climatic conditions, etc., so their power generation data has a certain reference value for the newly built electric fields. The power generation data of the newly built electric fields is used as the target domain data. Since the newly built electric fields lack historical data, it is necessary to use the source domain data to assist in generating the target domain data.

[0078] Furthermore, the generator and discriminator are used to process the source and target domain data to extract the time series features of the power generation data. This step helps capture the dynamic changes and underlying patterns of the data. The Grassmann manifold kernel function is used to map the source and target domain features to the manifold domain. The Grassmann manifold is a mathematical tool that describes the low-dimensional embedding structure of high-dimensional data and preserves both local and global structural information of the data.

[0079] Furthermore, in the manifold domain, the topological structure of the data remains unchanged, which helps address the problem of distortion in the original feature space. The geodesic distance between the source and target domains in the manifold domain is calculated. The geodesic distance is the shortest path length between two points on the manifold and reflects the true distance of the data on the manifold. This geodesic distance is used as the loss function for the sample set generation model. By minimizing this loss function, the generated sample set can be made closer to the true target domain data on the manifold.

[0080] Finally, the sample set generation model corresponding to the loss function is used to batch generate new energy market clearing sample sets. This step generates new energy market clearing sample sets that conform to the characteristics of the target domain data based on the features extracted in the previous steps, the mapping to the manifold domain, and the calculated geodesic distance.

[0081] This application uses transfer learning to use the data of adjacent electric fields as source domain data to assist in generating target domain data for newly built electric fields, effectively solving the problem of insufficient samples for newly built electric fields. By utilizing the topological invariance of manifold learning, the problem of data drift between domains is solved, making the generated sample set closer to the real target domain data on the manifold, thereby improving the quality of the data. This method can batch generate new energy market clearing sample sets, meet the needs of large-scale data processing, and improve data processing efficiency. The method for batch generation of new energy market clearing sample sets proposed in this application has significant advantages and innovations, and provides an effective solution to the problem of insufficient samples for newly built electric fields.

[0082] The following describes the implementation of the present application through specific embodiments. People familiar with this technology can easily understand other advantages and effects of the present application from the contents disclosed in this specification.

[0083] Please refer to Figure 2 , Figure 2 This is a flow chart of a new energy market clearing sample set batch generation technology in an embodiment of the present application. The method includes the following steps:

[0084] S101. The power generation of the adjacent electric field is used as the source domain data, and the power generation of the newly created electric field is used as the target domain data. The source domain data and the target domain data are input into the generator and the discriminator respectively to obtain the time series characteristics of the power generation data.

[0085] This application uses TCN as the feature extraction module for both the generator and the discriminator. The core advantage of TCN (Temporal Convolutional Network) lies in its ability to effectively capture local and long-term dependencies in time series data through convolution operations, while avoiding the vanishing and exploding gradient problems of traditional recurrent neural networks.

[0086] like Figure 3 As shown in the figure, the hidden layer is a stack of causal convolution layers. Compared with general convolutional neural networks, TCN introduces causal convolution to ensure that the model can predict the current time point. When outputting, the information of future time points will not be used, thus maintaining the causality of the time series. Compared with recurrent neural networks RNN and LSTM, TCN introduces dilated convolution to expand the receptive field of the convolution kernel. 2 and d The convolution step size is increased by 4, which enables the model to capture longer-term dependencies without increasing the number of parameters. Therefore, this application uses TCN to extract features from power generation data.

[0087] Power generation data of the source domain , target domain (target) power generation data For the input sequence , the mathematical representation of causal convolution is:

[0088] (1)

[0089] in, is the convolution kernel, is the convolution kernel size, is the input sequence.

[0090] In order to increase the receptive field without increasing the number of parameters, TCN uses dilated convolution. Dilated convolution, also known as hole convolution, inserts gaps between convolution kernels (i.e. skipping some input units), allowing the model to capture a larger receptive field without increasing the number of parameters, thereby better understanding the contextual information in the input data. The mathematical representation of dilated convolution is:

[0091] (2)

[0092] in, is the expansion rate.

[0093] Furthermore, the source domain features are obtained by extracting the time series features of power generation data. and target domain features .

[0094] S102. Map the source domain features and the target domain features to the manifold domain through the Grassmann manifold kernel function.

[0095] To address the distortion of the original feature space, this application maps source and target domain features to the manifold domain. In the Grassmann manifold, feature transformation and distribution adaptation typically have valid numerical forms, which can be efficiently represented and solved in transfer learning problems. Manifold learning can preserve the local structure and geometric characteristics of the data and maintain topological invariance in the distorted space, thus showing good robustness in manifold domain features.

[0096] For source domain features and target domain features , in the manifold domain Calculate their geodesics in In a manifold, a geodesic is a special curve that describes the shortest or most direct path between two points on the manifold. In a sufficiently small neighborhood, the properties of a geodesic are similar to those of a straight line in Euclidean space. However, in a larger area, the path of the geodesic may change due to the curvature of the manifold. The schematic diagram of the geodesic between the source domain and the target domain is shown in Figure 4 As shown, Represents the location of the source domain data in the manifold space, Represents the location of the target domain data in the manifold space, geodesic It is topologically invariant on manifolds.

[0097] This application uses the geodesic flow kernel function GFK (Geodesic Flow Kernel) to map the features in the original space to the manifold domain.

[0098] In this application, GFK is based on the kernel method, which calculates the similarity between data points by defining a kernel function without explicitly mapping the data into a high-dimensional space. This method can be seen as a arrive Incremental "walking" method. New features The transformed features and The inner product of defines a semi-positive definite geodesic flow kernel. The GFK kernel function is defined as follows:

[0099] (3)

[0100] Therefore, the features in the original space can be Transformed to the Grassmann manifold, note that It's just a form of expression.

[0101] S103. Calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model.

[0102] For source domain features mapped to manifold domain and target domain features , optimizing the geodesic distance between them, such as Figure 5 As shown, the sample set generation model can learn the data distribution of the target domain. Therefore, this application proposes a loss function based on the manifold geodesic distance to calculate the KL divergence of the source domain features and the target domain features on the manifold. The loss function is as follows:

[0103] (4)

[0104] Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

[0105] The advantage of the present disclosure is that the source domain data and the target domain are mapped to the manifold space, and the geodesic distance between the source domain data and the target data is optimized on the manifold. Compared with the traditional method of generating data using transfer learning, the present disclosure takes into account the problem of feature space distortion and uses the topological invariance of the manifold to introduce the geodesic distance into the market clearing sample set generation method for the first time.

[0106] Using the generated sample set to participate in electricity market clearing can supplement the lack of historical data, optimize the market clearing model, assist market players in decision-making, optimize the design and management of the electricity market, and promote the effective use of renewable energy.

[0107] like Figure 6 As shown, the third objective of this application is to provide a system for batch generation of new energy market clearing sample sets. This system is based on a method for batch generation of new energy market clearing sample sets. The system specifically includes: an extraction module 100, a mapping module 200, a calculation module 300, and the generation module 400.

[0108] Among them, the extraction module 100 is used to use the power generation of the adjacent electric field as the source domain data and the power generation of the newly built electric field as the target domain data, input the source domain data and the target domain data into the generator and the discriminator respectively, and extract the time series features of the power generation data to obtain the source domain features and target domain features respectively.

[0109] Furthermore, the extraction module 100 is specifically configured to:

[0110] Power generation data of the source domain , target domain power generation data , for the input sequence , causal convolution is expressed as:

[0111]

[0112] in, is the convolution kernel, is the convolution kernel size, yes The input sequence of moments;

[0113] Then the dilated convolution of TCN is expressed as:

[0114]

[0115] in, is the expansion rate;

[0116] The time series features of power generation data are extracted through TCN to obtain source domain features. and target domain features .

[0117] The mapping module 200 is used to map the source domain features and the target domain features to the manifold domain through the Grassmann manifold kernel function.

[0118] Furthermore, the mapping module is specifically configured to:

[0119] Map the source domain features and target domain features into the manifold domain; for the source domain features and target domain features , in the manifold domain Calculate the corresponding geodesic ;

[0120] The geodesic flow kernel function GFK is used to map the features in the original space to the manifold domain; the transformed features and The inner product of defines a semi-positive definite geodesic flow kernel, and the GFK kernel function is defined as follows:

[0121]

[0122] The features in the original space are obtained by Transformed to the Grassmann manifold, It is a form of expression.

[0123] The calculation module 300 is used to calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model.

[0124] Furthermore, the calculation module 300 is specifically configured to:

[0125] For source domain features mapped to manifold domain and target domain features , optimize source domain features and target domain features Based on the loss function of the manifold geodesic distance, the KL divergence of the source domain features and the target domain features on the manifold is calculated, and the loss function is as follows:

[0126]

[0127] Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

[0128] The generation module 400 is used to generate a batch of new energy market clearing sample sets using a sample set generation model corresponding to the loss function.

[0129] Furthermore, the generation module 400 is specifically configured to input the target domain data into a sample set generation model corresponding to the loss function to batch generate new energy market clearing sample sets.

[0130] like Figure 7As shown, the third object of the embodiment of the present application is to provide an electronic device, including a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor, wherein the processor implements the above-mentioned method for batch generation of new energy market clearing sample sets when executing the computer program. The electronic device also includes a communication interface 703 and a bus 704.

[0131] The fourth purpose of the embodiment of the present application is to provide a computer-readable storage medium, which stores a computer program, and when the computer program is executed by a processor, it implements the above-mentioned method for batch generation of new energy market clearing sample sets.

[0132] The fifth purpose of the embodiment of the present application is to provide a computer program product, which includes computer instructions, and the computer instructions instruct the computer to execute the above-mentioned method for batch generating new energy market clearing sample sets.

[0133] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0134] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A step that specifies a function in one or more boxes.

[0135] The present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware. Furthermore, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to magnetic disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0136] The computer program instructions can also be loaded onto a computer, other programmable data processing apparatus, or other processing device to cause a series of operational steps to be performed on the computer, other programmable apparatus or other processing device to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide processes for implementing the functions specified in the flowchart block or blocks. Figure 1 The flowchart and / or block diagram in the variation of the present application can describe any device, system or computer program products that perform one or more functions and can be implemented as hardware, software, or combinations thereof. The device, system or computer program products that perform one or more functions can be referred to as "circuit" or "module". In addition, these devices, systems or computer program products that perform one or more functions can have one or more flowcharts and / or block diagrams that can be referred to as "circuit" or "module". Figure 1 The flowchart and / or block diagram in the variation of the present application can describe any device, system or computer program products that perform one or more functions and can be implemented as hardware, software, or combinations thereof. The device, system or computer program products that perform one or more functions can be referred to as "circuit" or "module". In addition, these devices, systems or computer program products that perform one or more functions can have one or more flowcharts and / or block diagrams that can be referred to as "circuit" or "module".

[0137] Obviously, the embodiments described above are only some but not all of the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative work should be within the protection scope of the present application.

[0138] Finally, it should be noted that the above embodiments are used to explain the technical solutions of the present application but not to limit the technical solutions of the present application. Although the present application has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that the technical solutions of the present application can be modified or replaced equivalently without departing from the spirit and scope of the present application, and any modification or equivalent replacement should be covered within the protection scope of the claims of the present application.

Claims

1. A method for batch generation of new energy market clearing sample sets, characterized by: include: The power generation of the adjacent electric field is used as the source domain data, and the power generation of the newly built electric field is used as the target domain data. The source domain data and target domain data are input into the generator and discriminator respectively, and the time series features of the power generation data are extracted to obtain the source domain features and target domain features respectively. Through the Grassmann manifold kernel function, the source domain features and target domain features are mapped to the manifold domain; Calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model; Batch generation of new energy market clearing sample sets using a sample set generation model corresponding to the loss function; The source domain features and target domain features are mapped to the manifold domain through the Grassmann manifold kernel function, including: Map the source domain features and target domain features into the manifold domain; for the source domain features and target domain features , in the manifold domain Calculate the corresponding geodesic ; The geodesic flow kernel function GFK is used to map the features in the original space to the manifold domain; the transformed features and The inner product of defines a semi-positive definite geodesic flow kernel, and the GFK kernel function is defined as follows: The features in the original space are obtained by Transformed to the Grassmann manifold, It is a form of expression.

2. The method for batch generation of new energy market clearing sample sets according to claim 1, characterized in that: The generator and discriminator are time domain convolutional networks (TCNs).

3. The method for batch generation of new energy market clearing sample sets according to claim 2, characterized in that: The time series features of the power generation data are extracted to obtain source domain features and target domain features, which specifically include: Power generation data of the source domain , target domain power generation data , for the input sequence , causal convolution is expressed as: in, is the convolution kernel, is the convolution kernel size, yes The input sequence of moments; Then the dilated convolution of TCN is expressed as: in, is the expansion rate; The time series features of power generation data are extracted through TCN to obtain source domain features. and target domain features .

4. The method for batch generation of new energy market clearing sample sets according to claim 1, characterized in that: The step of calculating the geodesic distance between the source domain and the target domain in the manifold domain and using the geodesic distance as a loss function of the sample set generation model includes: For source domain features mapped to manifold domain and target domain features , optimize source domain features and target domain features Based on the loss function of the manifold geodesic distance, the KL divergence of the source domain features and the target domain features on the manifold is calculated, and the loss function is as follows: Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

5. The method for batch generation of new energy market clearing sample sets according to claim 1, characterized in that: The batch generation of new energy market clearing sample sets using the sample set generation model corresponding to the loss function includes: The target domain data is input into a sample set generation model corresponding to the loss function to generate new energy market clearing sample sets in batches.

6. A new energy market clearing sample set batch generation system, characterized by: include: The extraction module is used to use the power generation of the adjacent electric field as the source domain data and the power generation of the newly built electric field as the target domain data. The source domain data and target domain data are input into the generator and discriminator respectively, and the time series features of the power generation data are extracted to obtain the source domain features and target domain features respectively. A mapping module is used to map the source domain features and the target domain features to the manifold domain through the Grassmann manifold kernel function; A calculation module is used to calculate the geodesic distance between the source domain and the target domain in the manifold domain, and use the geodesic distance as the loss function of the sample set generation model; A generation module, configured to generate a batch of new energy market clearing sample sets using a sample set generation model corresponding to the loss function; The mapping module is specifically used to: Map the source domain features and target domain features into the manifold domain; for the source domain features and target domain features , in the manifold domain Calculate the corresponding geodesic ; The geodesic flow kernel function GFK is used to map the features in the original space to the manifold domain; the transformed features and The inner product of defines a semi-positive definite geodesic flow kernel, and the GFK kernel function is defined as follows: The features in the original space are obtained by Transformed to the Grassmann manifold, It is a form of expression.

7. A new energy market clearing sample set batch generation system according to claim 6, characterized in that: The generator and discriminator are time domain convolutional networks (TCNs).

8. The new energy market clearing sample set batch generation system according to claim 7 is characterized in that: The extraction module is specifically used to: Power generation data of the source domain , target domain power generation data , for the input sequence , causal convolution is expressed as: in, is the convolution kernel, is the convolution kernel size, yes The input sequence of moments; Then the dilated convolution of TCN is expressed as: in, is the expansion rate; The time series features of power generation data are extracted through TCN to obtain source domain features. and target domain features .

9. The new energy market clearing sample set batch generation system according to claim 6, characterized in that: The computing module is specifically configured to: For source domain features mapped to manifold domain and target domain features , optimize source domain features and target domain features Based on the loss function of the manifold geodesic distance, the KL divergence of the source domain features and the target domain features on the manifold is calculated, and the loss function is as follows: Where, for The probability density of the geodesic function, for The probability density of the geodesic function is given by Compute the KL divergence between two geodesics.

10. The new energy market clearing sample set batch generation system according to claim 6, characterized in that: The generation module is specifically used to: The target domain data is input into a sample set generation model corresponding to the loss function to generate new energy market clearing sample sets in batches.

11. An electronic device, characterized in that: It includes a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, it implements the method for batch generation of new energy market clearing sample sets as described in any one of claims 1 to 5.

12. A computer-readable storage medium, characterized in that The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the method for batch generating new energy market clearing sample sets according to any one of claims 1 to 5 is implemented.

13. A computer program product comprising computer instructions, characterized in that: The computer instructions instruct the computer to execute the method for batch generating new energy market clearing sample sets as described in any one of claims 1-5.

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