Multiplex parallel GAT-ALSTM: A novel spatial-temporal learning model for multi-sites wind power collaborative forecasting

Abstract

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In order to improve the accuracy of wind power output forecasting and ensure reliability of the power grid, multiplex parallel GAT-ALSTM, a spatial-temporal learning model for multi-sites wind power collaborative forecasting is proposed in this study. Topography was generated by using geographic information (longitude and latitude) obtained from the wind power generation sites. The GAT layer was used to capture the spatial correlation characteristics of multi-sites wind power. Feature dimension enhancement of each wind power generation site was achieved by aggregating the information from the adjacent sites. The ALSTM layer was used to capture the temporal correlation of each power output time series. The multiplex parallel structure of the model is designed to provide fast prediction of large-scale distributed wind power generation. The validity of the proposed multiplex parallel GAT-ALSTM was confirmed by comparison with the forecast results obtained by RNN, LSTM, ALSTM, and GNN-ALSTM. The testing results showed that, compared to RNN, LSTM, ALSTM, and GNN-ALSTM, the forecast results of the multiplex parallel GAT-ALSTM had the lowest mean absolute value error and the highest accuracy.

Keywords

• wind power forecast
• multi-sites
• multiplex parallel
• graph attention network
• attention-based LSTM