CN117556379A - Photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint - Google Patents
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Abstract
The invention relates to the field of photovoltaic power prediction, and discloses a photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint. By collecting historical data related to photovoltaic power generation, carrying out principle analysis and optimizing knowledge in the photovoltaic power generation field, carrying out feature amplification on original data by utilizing a sliding window feature amplification mechanism, and constructing a parallel feature extraction network to capture short-term and long-term dependency relationship. Then, the local and global information are effectively fused using a feature interaction and feature intersection fusion module. Finally, the domain knowledge is introduced into the prediction model, the theoretical knowledge is utilized to guide the model to train, the action improves the physical interpretability and accuracy of the model to a certain extent, and the problems of inaccurate and unreasonable power prediction are well solved.
Description
Technical Field
The invention belongs to the technical field of photovoltaic power prediction, and particularly relates to a photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint.
Background
In the global energy transformation large background, the photovoltaic power generation technology will become one of the most dominant new energy power generation technologies in the future. However, since photovoltaic power generation is affected by weather conditions, solar altitude, solar radiation intensity, temperature, humidity, photovoltaic power generation power has significant intermittence, fluctuation, which causes unstable power generation output thereof. Therefore, the improvement of the accuracy of photovoltaic power generation power prediction has important practical significance.
At present, a photovoltaic system generation power prediction method is mainly researched based on artificial intelligence technologies such as machine learning, deep learning and the like. The traditional method comprises the following steps: and predicting by using a support vector machine, a random forest and a differential integration moving average autoregressive model. In addition, a great deal of study is also being conducted on a photovoltaic power generation power combination prediction method by a great deal of students, such as: CNN-LSTM combination prediction method, stacking prediction method, XGBoost-LSTM combination prediction model. The traditional method has lower prediction accuracy, the combined prediction method has a certain improvement on the prediction accuracy, but ignores short-term and long-term dependence between data, and does not combine the actual situation of a photovoltaic system, and whether the prediction result is reasonable is considered.
Therefore, the depth feature fusion is utilized to mine short-term and long-term dependency relationship between related data of photovoltaic power generation, and the method has important significance for solving the problem that the accuracy of the current prediction result is low. Meanwhile, interaction among solar radiation, weather conditions and characteristic factors of the photovoltaic module is considered through principle analysis to mine field knowledge, the field knowledge is brought into a training process of the model, physical interpretability and accuracy of the model are improved, and a reliable tool can be provided for optimization and management of a photovoltaic power generation system.
Disclosure of Invention
The invention aims to provide a photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint so as to realize accurate prediction of photovoltaic power generation power.
Step 1: the photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint comprises the following steps:
step S10, collecting historical data related to photovoltaic power generation, including meteorological data, photovoltaic panel parameters, power generation, solar radiation and cleaning data; further considering the mechanism and the characteristic of the photovoltaic power generation, analyzing the temperature effect of the photovoltaic module, grid-connected access of the photovoltaic system and the physical principle of the photovoltaic power generation to determine the domain knowledge constraint;
s20, inputting the collected data into a characteristic amplification module; extracting overall trend features in data in each window by utilizing a sliding window feature amplification mechanism, and serially connecting the extracted features to increase the number of features;
step S30, designing a parallel feature extraction network, wherein the parallel feature extraction network comprises two sub-networks CNN and a Transformer, the sub-network 1 (CNN framework) extracts key local features, and the sub-network 2 (Transformer framework) extracts overall global features; constructing a feature interaction module by utilizing a linear self-attention mechanism and convolution position coding, and constructing a feature cross fusion module by zooming, splicing and linearizing features, so as to effectively fuse the feature information extracted from two frame trunks;
and S40, excavating knowledge in the photovoltaic power generation field according to a photovoltaic power generation mechanism, constructing an activation function and a loss function to realize field knowledge constraint, and updating model parameters by adopting a random gradient descent method to obtain optimal learning parameters.
Step 2: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint of the step 1, in the step S20, a sliding window feature amplification mechanism is specifically designed as follows: first, set upIs->Time series data of individual time points, wherein +.>Wherein->Is the length of the time series data; second, a single window size is set to +.>Dividing the data according to the window size to obtain window +.>Defined as->Wherein->For the window set +.>Is the number of windows; then, the slide step is set to +.>Wherein->For the data samples in each window, extracting overall trend information in time series data related to the photovoltaic power generation power by adopting an average value, analyzing long-term change and trend of the data, realizing smooth processing of abnormal values, and reducing noise influence; finally, the features extracted from the adjacent windows are connected in series, so that feature amplification is realized.
Step 3: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint in the step 1, in the step S30, local features and global features of data are obtained by utilizing the constructed parallel feature extraction network, interactive propagation between the two network features is realized through a linear self-attention mechanism, convolution position coding query interactive information is introduced, and the method is defined as follows:
wherein,representing feature interaction module->Is obtained by linear transformation of the input sequence, +.>The information of different positions in the input sequence is focused and extracted for linear attention, and the method is specifically as follows:
wherein,is->Dimension of pair->Go->An operation of transforming it into a probability distribution, +.>For transpose operation, by transpose->And->Performing dot product operation, and distributing the weight of each key to a corresponding value; by means ofThe inquiry interaction information is specifically as follows:
wherein,is Hadamard product, ">As a depth-separable convolution function,is a value vector after depth separable convolution;
the feature cross fusion module is defined as:
wherein,for the cross-fused feature, +.>For attention coefficient, +.>、/>Low-scale feature map and high-scale feature map, respectively, < >>For->Up-sampling operations are performed,/->For serial splicing function, < >>For convolution function +.>For the batch normalization function, +.>To activate the function, by dot multiplication +.>Will->Zoom and matchSerial splicing, using->Convolving the spliced features by +.>Operates to normalize each channel in the feature map and uses +.>And carrying out nonlinear activation on the characteristic map after batch normalization.
Step 4: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint in the step 1, in the step S40, positive constraint is realized by using an activation function through principle analysis, specifically:
wherein,for injecting the predicted value after positive constraint, +.>Is unconstrained predictive value, < >>For inputting data, define +.>Wherein->For sample set, ++>For the number of samples +.>For sample dimension +.>To learn parameters, update ++by random gradient descent method>Obtaining optimal learning parameters->To activate the function, use ∈>The function prevents the predicted result from generating negative values; according to the temperature effect analysis of the photovoltaic module, interval constraint is realized by using a loss function, and the model loss function is as follows:
wherein,、/>is weight(s)>The initial value is set to 0.5, and can be adjusted subsequently by whether the interval constraint is violated and contains an error greater than the true value; />The method is used for unconstrained prediction loss items, and specifically comprises the following steps:，/>for the number of samples +.>Is true value +.>Is an unconstrained predicted value; />The constraint penalty term for the interval is specifically:
wherein,、/>for maximum and minimum of the generated power, use +.>The function restriction model output falls within +.>A range; in the training process, the loss function is minimized by adjusting the weight, so that the accuracy of the model is improved.
The beneficial effects of the invention are as follows:
according to the invention, through principle analysis, domain knowledge is introduced into a photovoltaic power generation power prediction model, and the action is of practical significance to the photovoltaic power generation industry. The physical interpretability and accuracy of the model are improved to a certain extent by utilizing theoretical guiding model training, so that more accurate and reasonable prediction results are provided. Meanwhile, the invention reasonably uses the parallel feature extraction network to capture short-term and long-term dependency relationship of data in a parallel mode, and realizes interactive propagation between features of the two networks through self-attention linearization. In addition, the feature cross fusion module is utilized to explore the global-local information of the data, so that the accurate and reasonable prediction of the photovoltaic power generation power is realized.
Drawings
FIG. 1 is an algorithm architecture;
fig. 2 is a feature interaction-intersection fusion illustration.
Detailed Description
Embodiments of the invention are described in detail below with reference to the attached drawings, but the invention can be implemented in a number of different ways, which are defined and covered by the claims.
Under the constraint of field knowledge, the photovoltaic power generation power prediction method utilizes global-local information of data to predict photovoltaic power generation power through feature fusion.
Firstly, collecting historical data related to photovoltaic power generation, including meteorological data, photovoltaic panel parameters, power generation power and solar radiation, and carrying out principle analysis after data cleaning to optimize knowledge in the photovoltaic power generation field; secondly, performing characteristic amplification on the collected data by utilizing a sliding window characteristic amplification mechanism; then, extracting local and global features by adopting a parallel feature extraction network, and realizing feature fusion by utilizing a feature interaction module and a feature cross fusion module so as to explore global-local information; and finally, according to principle analysis, the domain knowledge is injected into the prediction model, and the accuracy and rationality of the prediction result are improved by guiding the model training process.
Step 1: the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint is shown as a figure 1, and shows an algorithm system structure of the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint, and comprises the following steps:
step S10, collecting historical data related to photovoltaic power generation, including meteorological data, photovoltaic panel parameters, power generation, solar radiation and cleaning data; further considering the mechanism and the characteristic of the photovoltaic power generation, analyzing the temperature effect of the photovoltaic module, grid-connected access of the photovoltaic system and the physical principle of the photovoltaic power generation to determine the domain knowledge constraint;
s20, inputting the collected data into a characteristic amplification module; extracting overall trend features in data in each window by utilizing a sliding window feature amplification mechanism, and serially connecting the extracted features to increase the number of features;
step S30, designing a parallel feature extraction network, wherein the parallel feature extraction network comprises two sub-networks CNN and a Transformer, the sub-network 1 (CNN framework) extracts key local features, and the sub-network 2 (Transformer framework) extracts overall global features; constructing a feature interaction module by utilizing a linear self-attention mechanism and convolution position coding, and constructing a feature cross fusion module by zooming, splicing and linearizing features, so as to effectively fuse the feature information extracted from two frame trunks;
and S40, excavating knowledge in the photovoltaic power generation field according to a photovoltaic power generation mechanism, constructing an activation function and a loss function to realize field knowledge constraint, and updating model parameters by adopting a random gradient descent method to obtain optimal learning parameters.
Step 2: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint of the step 1, in the step S20, a sliding window feature amplification mechanism is specifically designed as follows: first, set upIs->Time series data of individual time points, wherein +.>Wherein->Is the length of the time series data; second, a single window size is set to +.>Dividing the data according to the window size to obtain window +.>Defined as->Wherein->For the window set +.>Is the number of windows; then, the slide step is set to +.>Wherein->For the data samples in each window, adopting average value to extract characteristics, realizing the smooth processing of abnormal values and reducing noise influence; and finally, splicing the extracted features in the adjacent windows to realize feature amplification.
Step 3: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint of the step 1, in the step S30, a constructed parallel feature extraction network is utilized to obtain data local features and global features; fig. 2 is a feature interaction-intersection fusion diagram shown in the application, and as shown in fig. 2, in order to effectively fuse local features and global features of data, a feature interaction module and a feature intersection fusion module are constructed, and a construction method thereof includes:
interactive propagation between two network features is achieved through a linear self-attention mechanism, defined as:
wherein,representing feature interaction module->From inputThe sequences are linearly transformed to give->The information of different positions in the input sequence is focused and extracted for linear attention, and the method is specifically as follows:
wherein,is->Dimension of pair->Go->An operation of transforming it into a probability distribution, +.>For transpose operation, by transpose->And->Performing dot product operation, and distributing the weight of each key to a corresponding value;
introducing convolution position coding inquiry interaction information, specifically:
wherein,interactive information obtained for inquiry +.>In the form of a Hadamard product,for depth separable convolution function, < >>Is a value vector after depth separable convolution;
the feature cross fusion module is defined as:
wherein,for the cross-fused feature, +.>For attention coefficient, +.>、/>Low-scale feature map and high-scale feature map, respectively, < >>For->Up-sampling operations are performed,/->For serial splicing function, < >>For convolution function +.>For the batch normalization function, +.>To activate the function, by dot multiplication +.>Will->Zoom and matchSerial splicing, using->Convolving the spliced features by +.>Operates to normalize each channel in the feature map and uses +.>And carrying out nonlinear activation on the characteristic map after batch normalization.
Step 4: according to the photovoltaic power generation power prediction method based on depth feature fusion under the domain knowledge constraint in the step 1, in the step S40, positive constraint is realized by using an activation function through principle analysis, specifically:
wherein,for injecting the predicted value after positive constraint, +.>Is unconstrained predictive value, < >>For inputting data, define +.>Wherein->For a sample set，/>For the number of samples +.>For sample dimension +.>To learn parameters, update ++by random gradient descent method>Obtaining optimal learning parameters->To activate the function, use ∈>The function prevents the predicted result from generating negative values; according to the temperature effect analysis of the photovoltaic module, interval constraint is realized by using a loss function, and the model loss function is as follows:
wherein,、/>is weight(s)>The initial value is set to 0.5, and can be adjusted subsequently by whether the interval constraint is violated and contains an error greater than the true value; />The method is used for unconstrained prediction loss items, and specifically comprises the following steps:，/>for the number of samples +.>Is true value +.>Is an unconstrained predicted value; />The constraint penalty term for the interval is specifically:
wherein,、/>for maximum and minimum of the generated power, use +.>The function restriction model output falls within +.>A range; in the training process, the loss function is minimized by adjusting the weight, so that the accuracy of the model is improved.
The foregoing is merely representative of embodiments of the present invention and is not intended to limit the scope of the present invention. Any equivalent structures or equivalent flow transformations based on the description of the present invention and the drawings, as well as direct or indirect applications in other related arts, are within the scope of the present invention.
Claims (4)
1. The photovoltaic power generation power prediction method based on depth feature fusion under domain knowledge constraint is characterized by comprising the following steps of:
step S10, collecting historical data related to photovoltaic power generation, including meteorological data, photovoltaic panel parameters, power generation, solar radiation and cleaning data; analyzing the temperature effect of the photovoltaic module, grid-connected access of the photovoltaic system and extraction of field knowledge constraint of the physical principle of photovoltaic power generation;
s20, inputting the collected data into a characteristic amplification module; extracting overall trend features in data in each window by utilizing a sliding window feature amplification mechanism, and serially connecting the extracted features to increase the number of features;
step S30, designing a parallel feature extraction network, wherein the parallel feature extraction network comprises two sub-networks CNN and a Transformer, the sub-network 1 (CNN framework) extracts key local features, and the sub-network 2 (Transformer framework) extracts overall global features; constructing a feature interaction module by utilizing a linear self-attention mechanism and convolution position coding, and constructing a feature cross fusion module by zooming, splicing and linearizing features, so as to effectively fuse the feature information extracted from two frame trunks;
and S40, excavating knowledge in the photovoltaic power generation field according to a photovoltaic power generation mechanism, constructing an activation function and a loss function to realize field knowledge constraint, and updating model parameters by adopting a random gradient descent method to obtain optimal learning parameters.
2. The method for predicting the photovoltaic power generation power by depth feature fusion under the domain knowledge constraint according to claim 1, wherein in the step S20, the sliding window feature amplification mechanism is specifically designed as follows: first, set upIs->Time series data of individual time points, wherein +.>Wherein->As time series dataA length; second, a single window size is set to +.>Dividing the data according to the window size to obtain window +.>Defined as->Wherein->For the window set +.>Is the number of windows; then, the slide step is set to +.>Wherein->For the data samples in each window, extracting features by adopting an average value; and finally, serially connecting the extracted features in the adjacent windows.
3. The method for predicting the photovoltaic power generation power by depth feature fusion under the domain knowledge constraint according to claim 1, wherein in the step S30, the local features and the global features of the data are obtained by using the constructed parallel feature extraction network, the interactive propagation between the two network features is realized by a linear self-attention mechanism, and the convolutional position coding query interactive information is introduced, which is defined as:
wherein,representing feature interaction module->Is obtained by performing a linear transformation on the input sequence,the information of different positions in the input sequence is focused and extracted for linear attention, and the method is specifically as follows:
wherein,is->Dimension of pair->Go->An operation of transforming it into a probability distribution, +.>For transpose operation, by transpose->And->Performing dot product operation, and distributing the weight of each key to a corresponding value; by means ofThe inquiry interaction information is specifically as follows:
wherein,is Hadamard product, ">As a depth-separable convolution function,is a value vector after depth separable convolution;
the feature cross fusion module is defined as:
wherein,for the cross-fused feature, +.>For attention coefficient, +.>、/>Low-scale feature map and high-scale feature map, respectively, < >>For->Up-sampling operations are performed,/->As a function of the serial concatenation,/>for convolution function +.>For the batch normalization function, +.>To activate the function, by dot multiplication +.>Will->Zoom and->Serial splicing, using->Performing convolution operation on the serial spliced characteristics by +.>Operates to normalize each channel in the feature map and uses +.>And carrying out nonlinear activation on the characteristic map after batch normalization.
4. The method for predicting the photovoltaic power generation power by depth feature fusion under domain knowledge constraint according to claim 1, wherein in the step S40, positive constraint is realized by using an activation function through principle analysis, specifically:
wherein,for injecting the predicted value after positive constraint, +.>Is unconstrained predictive value, < >>For inputting data, define +.>Wherein->For sample set, ++>For the number of samples +.>For sample dimension +.>To learn parameters, update ++by random gradient descent method>Obtaining optimal learning parameters->Is an activation function; according to the temperature effect analysis of the photovoltaic module, interval constraint is realized by using a loss function, and the model loss function is as follows:
wherein,、/>is weight(s)>The method is used for unconstrained prediction loss items, and specifically comprises the following steps:
wherein,for the number of samples +.>Is true value +.>Is an unconstrained predicted value; />The constraint penalty term for the interval is specifically:
wherein,、/>for maximum and minimum of the generated power, use +.>Function constraint model output falls intoRange.
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Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN119089396A (en) * | 2024-11-06 | 2024-12-06 | 成都大学 | A photovoltaic power prediction method combining global convolution and attention mechanism |
| CN119089396B (en) * | 2024-11-06 | 2025-01-28 | 成都大学 | A photovoltaic power prediction method combining global convolution and attention mechanism |
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