Ultra-short-term power forecasting for multiple wind fields based on SHAP importance ranking and spatio-temporal dual flow
To address the insufficient spatio-temporal feature extraction in multi-wind field wind power prediction, this paper proposes a power prediction method based on spatial and temporal dual-stream feature extraction.First, the Shapley Additive Explanations ( SHAP) method is employed to analyze the importance of each variable in the original high-dimensional Numerical Weather Prediction ( NWP) , and a subset of variables with high contribution is selected as the input to the prediction model, reducing the complexity of the model.Second, an Improved Graph Attention Network ( IGAT) based on an adaptive dynamic adjacency matrix is built to extract the dynamic spatial features of multiple wind fields.Meanwhile, Multi-Head Attention Mechanism ( MHA) is integrated with Temporal Convolutional Network ( TCN) to enhance the learning of key temporal features.Then, a feed-forward neural network is used to output the power prediction results of multiple wind fields.Finally, a case study is conducted with data from ten wind fields in Northwest China, and our results show the proposed model performs better in prediction than other models.
multi-wind field power predictionvariable selectiongraph attention networkmulti-head attention mechanismtemporal convolutional network
国家科技期刊平台
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