Transformer Encoder-LSTM Based Short-Term Wind Power Forecasting Considering Multiple Variables
Accurate and reliable short-term wind power prediction is crucial for the stable operation of the power grid.To address the instability of wind power generation,this paper proposes a hybrid architecture combining Transformer Encoder and LSTM,considering various influencing factors to model and predict this complex time series data.Experimental results indicate that the proposed Transformer Encoder-LSTM model achieves significant performance improvements in the wind power prediction task,outperforming single LSTM and GRU models in terms of Mean Absolute Error(MAE),Root Mean Square Error(RMSE),and Mean Absolute Percentage Error(MAPE).
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