Power load prediction model and method based on multi-modal fusion

Abstract

The invention discloses a power load prediction model and method based on multi-modal fusion. The power load prediction model comprises historical power consumption data, seasonal change data, weather condition data and special event data. The data are subjected to standardization processing firstly, so that the consistency and comparability of the input data are ensured; then each type of data is coded into a feature vector, or feature mapping is carried out through a full connection layer so as to adapt to subsequent deep learning processing; the prediction model algorithm adopts a long short-term memory (LSTM) network to process historical power consumption data of a time sequence so as to capture a time dependence characteristic of the data; the method comprises the following steps of: introducing a cross-attention mechanism into a prediction model, analyzing and fusing information in different data sources through a multi-head self-attention Multi-Head Attention model, and enabling the model to understand the mutual relationship and complementarity among data when processing multi-modal data.

Description

Technical Field

[0001] The present invention belongs to the technical field of power load forecasting and relates to a power load forecasting model and method based on multi-modal fusion. Background Art

[0002] In the field of power load forecasting, traditional forecasting models usually face some technical challenges and limitations, which mainly include data uniformity, insufficient information fusion, and weak model generalization ability.

[0003] Traditional models often rely solely on historical electricity consumption data to predict future electricity loads, ignoring the impact of factors such as weather, seasonal changes, and special events; this single data source limits the model's prediction accuracy and applicability, because electricity load is affected by a combination of multiple factors, and a single data source is difficult to fully reflect these influencing factors.

[0004] Even some models that attempt to integrate multiple data sources often fail to fully utilize the information between various data sources due to the lack of an effective information fusion mechanism; traditional fusion methods may not be able to effectively handle the inherent connections between different data types, resulting in important information being ignored or misinterpreted.

[0005] Many traditional forecasting models perform well under specific conditions, but their forecasting accuracy drops significantly when faced with different environments and changing conditions; this problem of weak generalization ability limits the application of the model in different regions or seasons. Summary of the invention

[0006] The technical problem to be solved by the present invention is to provide a power load forecasting model and method based on multimodal fusion, optimize the information processing flow, and enhance the generalization ability and prediction accuracy of the model in power load forecasting.

[0007] To solve the above technical problems, the technical solution adopted by the present invention is: a power load forecasting model based on multimodal fusion, the forecasting model includes historical power consumption data, seasonal change data, weather condition data and special event data; these data are first standardized to ensure the consistency and comparability of the input data; then each type of data is encoded into a feature vector, or feature mapped through a fully connected layer to adapt to subsequent deep learning processing; the forecasting model algorithm uses a long short-term memory network LSTM to process the historical power consumption data of the time series to capture the time-dependent characteristics of the data; the forecasting model introduces a cross-attention mechanism Cross-Attention Mechanism, and uses a multi-head self-attention Multi-HeadAttention model to analyze and fuse information from different data sources, so that the model can understand the relationship and complementarity between the data when processing multimodal data.

[0008] The data feature extraction and encoding method of the power load forecasting model based on multimodal fusion as described above includes the following steps: S1, historical electricity consumption data; S1-1, model selection, uses long short-term memory network LSTM to process time series data and capture long-term dependencies in electricity consumption data; S1-2, feature vectorization, selects a single hidden layer LSTM with a dimension of 128, which can handle complex time series features without over-complicating the model; S1-3, output vector, the output layer passes through a fully connected layer and outputs a feature vector with a dimension of 128 to ensure the compatibility of the cross-attention mechanism with other modalities in the subsequent process; S2, seasonal variation data; S2-1, encoding method, uses One-hot encoding to divide a year into four seasons: spring, summer, autumn, and winter, with each season corresponding to an independent encoding bit; S2-2, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a labeled vector to reflect the impact of seasonal changes on power load in subsequent calculations; S3, weather condition data; S3-1, key variables, temperature, humidity, wind speed, precipitation, and sunshine duration are factors that directly affect electricity demand and production capacity; S3-2, data preprocessing, Min-Max normalization is performed on all weather data, and the data range is adjusted to [0,1] to eliminate the impact of different measurement units and magnitudes and balance the weight of the data in the model; S3-3, feature vectorization, converts the processed single features, including temperature range, wind speed category, normalized humidity and precipitation, into a more abstract expression through a fully connected layer, that is, the dimension of the fully connected layer is consistent with other encoding information, using 128 dimensions; S3-4, output vector, the final weather condition output feature vector is set to 128 dimensions to ensure consistency with other modal data dimensions; S4, special event data; S4-1, encoding method, uses multi-label one-hot encoding to represent the type of special events; each type of event is assigned an independent bit, that is, holidays are [1,0,0,0], sports events are [0,1,0,0], and cultural activities are [0,0,1,0]; S4-2, event summary, summarizes and codes multiple events that occur at the same time, and codes [1,1,0,0] when there are holidays and sports events at the same time; S4-3, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a tag vector to reflect the impact of event changes on power load in subsequent calculations; for multi-event encoding, all corresponding tag vectors are weighted summed.

[0009] The cross-attention mechanism method of the power load forecasting model based on multimodal fusion as described above includes the following steps: SA1, multimodal fusion; SA1-1, vector after feature extraction. After feature extraction, each input data will be converted into a fixed-length vector of 128 dimensions; SA1-2, fusion process, the feature fusion module is based on the cross attention mechanism and uses a multi-head self-attention Multi-Head Attention mechanism to process these feature vectors. It has 8 heads and a dimension of 128. The Attention layer will receive the encoding vectors of all modalities as input, generate a comprehensive feature representation by learning the relationship and complementarity between different modal data, and output a single vector that integrates all important information, providing rich context information for the subsequent decoding process; SA2, fusion basis; SA2-1, query Q, key K, value V, the encoding vectors of all modalities are simultaneously input into the Attention layer as Q, K, and V, allowing the model to learn internal dependencies by itself; after being processed and extracted by the encoder, the feature data will be combined to participate in the calculation of the Attention weight matrix; because the query side query is unique in the Attention mechanism, each feature will have a chance to become the query side, so as to match the attention with other features; SA2-2, output, the output of the Attention layer is a vector that fuses all input modal information, which is then used to predict power load; SA3, prediction layer; SA3-1, fully connected layer, the vector output from the Attention layer will be passed to one or more fully connected layers DenseLayers for final prediction; SA3-2, Output,The final output of the model is the prediction of future power load, which is a continuous load value or power demand in a specific time period; SA4, model training and optimization; SA4-1, loss function, uses mean square error MSE as the loss function to optimize the performance of the model in power load forecasting; SA4-2, optimization algorithm, uses Adam optimizer to process large-scale data sets.

[0010] The main beneficial effects of the present invention are: It can integrate and optimize the processing of multimodal data; the data of each modality is converted into feature vectors with consistent dimensions, which are then input into the self-attention layer for in-depth analysis; the output of the self-attention layer is a vector that integrates all important information, which is ultimately used to predict power load; such a structure not only optimizes the information processing process, but also enhances the generalization ability and prediction accuracy of the model in the field of power load forecasting.

[0011] It enables the model to better understand the relationships and complementarities between data when processing multimodal data.

[0012] This ensures that the encoding method of each modality data can maintain its unique information characteristics while ensuring the uniformity of vector length, which is suitable for subsequent cross-attention operations.

[0013] Through the fusion of this multimodal data, the model can more comprehensively understand and predict future changes in electricity demand, significantly improving the accuracy of predictions compared to traditional models that rely on a single data source.

[0014] Multimodal fusion also improves the model's ability to capture nonlinear and complex relationships, enabling it to maintain high flexibility and robustness in various operating environments.

[0015] Improving forecast accuracy can help power companies optimize power generation and distribution plans and reduce power waste or shortages caused by forecast errors. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0017] Figure 1 This is a model structure diagram of the present invention. DETAILED DESCRIPTION

[0018] like Figure 1In this paper, a power load forecasting model based on multimodal fusion is proposed. The forecasting model includes historical power consumption data, seasonal change data, weather condition data and special event data. These data are first standardized to ensure the consistency and comparability of the input data. Then each data is encoded into a feature vector or feature mapped through a fully connected layer to adapt to subsequent deep learning processing. The forecasting model algorithm uses a long short-term memory network (LSTM) to process the historical power consumption data of the time series to capture the time-dependent characteristics of the data. The forecasting model introduces a cross-attention mechanism, which uses a multi-head self-attention Multi-Head Attention model to analyze and fuse information from different data sources, so that the model can understand the relationship and complementarity between the data when processing multimodal data.

[0019] Preferably, the key innovation of the above model is that it can integrate and optimize the processing of multimodal data; each modality of data is converted into a feature vector with consistent dimensions, which is then input into the self-attention layer for in-depth analysis. The output of the self-attention layer is a vector that incorporates all important information, which is ultimately used to predict power load. Such a structure not only optimizes the information processing process, but also enhances the generalization ability and prediction accuracy of the model in the field of power load forecasting.

[0020] Preferably, the model integrates data from multiple different modalities, including historical electricity consumption data, seasonal changes, weather conditions, and special events, each of which can provide unique information that affects electricity demand. For example, weather conditions such as temperature and humidity directly affect people's electricity consumption behavior, while special events such as holidays may cause peak electricity consumption. Through this fusion of multimodal data, the model can more comprehensively understand and predict future changes in electricity demand, which can significantly improve the accuracy of predictions compared to traditional models that rely on a single data source.

[0021] In a preferred solution, the data feature extraction and encoding method of the power load forecasting model based on multimodal fusion as described above comprises the following steps: S1, historical electricity consumption data; S1-1, model selection, uses long short-term memory network LSTM to process time series data and capture long-term dependencies in electricity consumption data; S1-2, feature vectorization, selects a single hidden layer LSTM with a dimension of 128, which can handle complex time series features without over-complicating the model; S1-3, output vector, the output layer passes through a fully connected layer and outputs a feature vector with a dimension of 128 to ensure the compatibility of the cross-attention mechanism with other modalities in the subsequent process; S2, seasonal variation data; S2-1, encoding method, uses One-hot encoding to divide a year into four seasons: spring, summer, autumn, and winter, with each season corresponding to an independent encoding bit; S2-2, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a labeled vector to reflect the impact of seasonal changes on power load in subsequent calculations; S3, weather condition data; S3-1, key variables, temperature, humidity, wind speed, precipitation, and sunshine duration are factors that directly affect electricity demand and production capacity; S3-2, data preprocessing, Min-Max normalization is performed on all weather data, and the data range is adjusted to [0,1] to eliminate the impact of different measurement units and magnitudes and balance the weight of the data in the model; S3-3, feature vectorization, converts the processed single features, including temperature range, wind speed category, normalized humidity and precipitation, into a more abstract expression through a fully connected layer, that is, the dimension of the fully connected layer is consistent with other encoding information, using 128 dimensions; S3-4, output vector, the final weather condition output feature vector is set to 128 dimensions to ensure consistency with other modal data dimensions; S4, special event data; S4-1, encoding method, uses multi-label one-hot encoding to represent the type of special events; each type of event is assigned an independent bit, that is, holidays are [1,0,0,0], sports events are [0,1,0,0], and cultural activities are [0,0,1,0]; S4-2, event summary, summarizes and codes multiple events that occur at the same time, and codes [1,1,0,0] when there are holidays and sports events at the same time; S4-3, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a tag vector to reflect the impact of event changes on power load in subsequent calculations; for multi-event encoding, all corresponding tag vectors are weighted summed.

[0022] Preferably, in the above method, it is ensured that the encoding method of each modal data can not only maintain its unique information characteristics but also ensure the uniformity of the vector length, which is convenient for subsequent cross-attention operations.

[0023] In a preferred solution, the cross-attention mechanism method of the power load forecasting model based on multimodal fusion as described above comprises the following steps: SA1, multimodal fusion; SA1-1, vector after feature extraction. After feature extraction, each input data will be converted into a fixed-length vector of 128 dimensions; SA1-2, fusion process, the feature fusion module is based on the cross attention mechanism and uses a multi-head self-attention Multi-Head Attention mechanism to process these feature vectors. It has 8 heads and a dimension of 128. The Attention layer will receive the encoding vectors of all modalities as input, generate a comprehensive feature representation by learning the relationship and complementarity between different modal data, and output a single vector that integrates all important information, providing rich context information for the subsequent decoding process; SA2, fusion basis; SA2-1, query Q, key K, value V, the encoding vectors of all modalities are simultaneously input into the Attention layer as Q, K, and V, allowing the model to learn internal dependencies by itself; after being processed and extracted by the encoder, the feature data will be combined to participate in the calculation of the Attention weight matrix; because the query side query is unique in the Attention mechanism, each feature will have a chance to become the query side, so as to match the attention with other features; SA2-2, output, the output of the Attention layer is a vector that fuses all input modal information, which is then used to predict power load; SA3, prediction layer; SA3-1, fully connected layer, the vector output from the Attention layer will be passed to one or more fully connected layers DenseLayers for final prediction; SA3-2, Output,The final output of the model is the prediction of future power load, which is a continuous load value or power demand in a specific time period; SA4, model training and optimization; SA4-1, loss function, uses mean square error MSE as the loss function to optimize the performance of the model in power load forecasting; SA4-2, optimization algorithm, uses Adam optimizer to process large-scale data sets.

[0024] In the above method, through the cross-attention mechanism, the model not only statically fuses data, but can dynamically learn the mutual influence and complementarity between different data sources. This mechanism enables the model to maintain good prediction performance when encountering changes in data distribution or different types of power load scenarios. In addition, multimodal fusion also improves the model's ability to capture nonlinear and complex relationships, allowing it to maintain high flexibility and robustness in various operating environments.

[0025] Accurate power load forecasting is crucial for the dispatch and management of power grids. Improving the accuracy of forecasts can help power companies optimize power generation and distribution plans and reduce power waste or shortages caused by forecast errors. Especially today when renewable energy is increasing, how to effectively integrate traditional energy and renewable energy to ensure the stability of the power grid, this high-precision forecasting model provides strong technical support.

[0026] The power load forecasting model based on multimodal fusion, through its high data integration capability and advanced machine learning technology, not only improves the accuracy of power load forecasting, but also provides effective technical support for the sustainable management of power systems. It has broad application prospects and important practical significance.

[0027] The above-mentioned embodiments are only preferred technical solutions of the present invention and should not be regarded as limitations of the present invention. The embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. The protection scope of the present invention shall be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A power load forecasting model based on multimodal fusion, characterized by: The prediction model includes historical electricity consumption data, seasonal change data, weather condition data and special event data; these data are first standardized to ensure the consistency and comparability of the input data; then each type of data is encoded into a feature vector, or feature mapped through a fully connected layer to adapt to subsequent deep learning processing; the prediction model algorithm uses a long short-term memory network LSTM to process the time series of historical electricity consumption data to capture the time-dependent characteristics of the data; the prediction model introduces a cross-attention mechanism, which uses a multi-head self-attention Multi-Head Attention model to analyze and fuse information from different data sources, allowing the model to understand the relationship and complementarity between the data when processing multimodal data.

2. The data feature extraction and encoding method of the power load forecasting model based on multimodal fusion according to claim 1 is characterized in that: The steps include: S1, historical electricity consumption data; S1-1, model selection, uses long short-term memory network LSTM to process time series data and capture long-term dependencies in electricity consumption data; S1-2, feature vectorization, selects a single hidden layer LSTM with a dimension of 128, which can handle complex time series features without over-complicating the model; S1-3, output vector, the output layer passes through a fully connected layer and outputs a feature vector with a dimension of 128 to ensure the compatibility of the cross-attention mechanism with other modalities in the subsequent process; S2, seasonal variation data; S2-1, encoding method, uses One-hot encoding to divide a year into four seasons: spring, summer, autumn, and winter, with each season corresponding to an independent encoding bit; S2-2, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a labeled vector to reflect the impact of seasonal changes on power load in subsequent calculations; S3, weather condition data; S3-1, key variables, temperature, humidity, wind speed, precipitation, and sunshine duration are factors that directly affect electricity demand and production capacity; S3-2, data preprocessing, Min-Max normalization is performed on all weather data, and the data range is adjusted to [0, 1] to eliminate the impact of different measurement units and magnitudes and balance the weight of the data in the model; S3-3, feature vectorization, converts the processed single features, including temperature range, wind speed category, normalized humidity and precipitation, into a more abstract expression through a fully connected layer, that is, the dimension of the fully connected layer is consistent with other encoding information, using 128 dimensions; S3-4, output vector, the final weather condition output feature vector is set to 128 dimensions to ensure consistency with other modal data dimensions; S4, special event data; S4-1, encoding method, uses multi-label one-hot encoding to represent the type of special events; each type of event is assigned an independent bit, that is, holidays are [1,0,0,0], sports events are [0,1,0,0], and cultural activities are [0,0,1,0]; S4-2, event summary, summarizes and codes multiple events that occur at the same time, and codes [1,1,0,0] when there are holidays and sports events at the same time; S4-3, feature mapping, through a linear transformation, i.e., a fully connected layer, with an output dimension of 128, maps the One-hot encoding into a tag vector to reflect the impact of event changes on power load in subsequent calculations; for multi-event encoding, all corresponding tag vectors are weighted summed.

3. The cross-attention mechanism method of the power load forecasting model based on multimodal fusion according to claim 1 is characterized in that: The steps include: SA1, multimodal fusion; SA1-1, vector after feature extraction. After feature extraction, each input data will be converted into a fixed-length vector of 128 dimensions; SA1-2, fusion process, the feature fusion module is based on the cross attention mechanism and uses a multi-head self-attention Multi-Head Attention mechanism to process these feature vectors. It has 8 heads and a dimension of 128. The Attention layer will receive the encoding vectors of all modalities as input, generate a comprehensive feature representation by learning the relationship and complementarity between different modal data, and output a single vector that integrates all important information, providing rich context information for the subsequent decoding process; SA2, fusion basis; SA2-1, query Q, key K, value V, the encoding vectors of all modalities are simultaneously input into the Attention layer as Q, K, and V, allowing the model to learn internal dependencies by itself; after being processed and extracted by the encoder, the feature data will be combined to participate in the calculation of the Attention weight matrix; because the query side query is unique in the Attention mechanism, each feature will have a chance to become the query side, so as to match the attention with other features; SA2-2, output, the output of the Attention layer is a vector that fuses all input modal information, which is then used to predict power load; SA3, prediction layer; SA3-1, fully connected layer, the vector output from the Attention layer will be passed to one or more fully connected layers DenseLayers for final prediction; SA3-2, Output,The final output of the model is the prediction of future power load, which is a continuous load value or power demand in a specific time period; SA4, model training and optimization; SA4-1, loss function, uses mean square error MSE as the loss function to optimize the performance of the model in power load forecasting; SA4-2, optimization algorithm, uses Adam optimizer to process large-scale data sets.

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