Interpretable Wind Power Short-Term Power Prediction Model Using Deep Graph Attention Network

Jinhua Zhang, Hui Li (), Peng Cheng and Jie Yan
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Jinhua Zhang: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Hui Li: School of Electrical Engineering, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Peng Cheng: School of Mathematics and Statistics, North China University of Water Resources and Electric Power, Zhengzhou 450045, China
Jie Yan: School of New Energy, North China Electric Power University, Beijing 100096, China

Energies, 2024, vol. 17, issue 2, 1-16

Abstract: High-precision spatial-temporal wind power prediction technology is of great significance for ensuring the safe and stable operation of power grids. The development of artificial intelligence technology provides a new scheme for modeling with strong spatial-temporal correlation. In addition, the existing prediction models are mostly ‘black box’ models, lacking interpretability, which may lead to a lack of trust in the model by power grid dispatchers. Therefore, improving the model to obtain interpretability has become an important challenge. In this paper, an interpretable short-term wind power prediction model based on ensemble deep graph neural network is designed. Firstly, the graph network model (GNN) with an attention mechanism is applied to the aggregate and the spatial-temporal features of wind power data are extracted, and the interpretable ability is obtained. Then, the long short-term memory (LSTM) method is used to process the extracted features and establish a wind power prediction model. Finally, the random sampling algorithm is used to optimize the hyperparameters to improve the learning rate and performance of the model. Through multiple comparative experiments and a case analysis, the results show that the proposed model has a higher prediction accuracy than other traditional models and obtains reasonable interpretability in time and space dimensions.

Keywords: wind power prediction; graph neural network; attention mechanism; interpretability; spatial-temporal characteristics (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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