Wind Power Forecasting Method Based on Bidirectional Long Short-Term Memory Neural Network and Error Correction

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With the improvement of penetration rate of wind power in the power system, its volatility and intermittence bring new problems to the power grid. The accurate forecasting of wind power is an effective way to alleviate the impact on the power grid. In this paper, a novel wind power forecasting method is proposed. Firstly, a wind power forecasting model based on the bidirectional long short-term memory (BiLSTM) neural network is established to forecast the wind power according to the wind speed of numerical weather forecasting (NWF). Secondly, a wind power forecasting error time series model based on empirical mode decomposition (EMD) is established to decrease the forecasting error. Finally, this paper uses the real data to simulate and verify the proposed method. Evaluating by the root mean square error (RMSE), symmetric mean absolute percentage error (SMAPE), and Theil inequality coefficient (TIC), the simulation results show that the forecasting accuracy of the BiLSTM neural network model are 10.25%, 6.71% and 12.18% higher than LSTM model respectively. After correcting wind power forecasting error using the proposed time series model based on EMD, the accuracy of wind power forecasting is further improved.