Ultra-short-term forecasting of wind power based on multi-task learning and LSTM

In order to achieve high precision ultra-short-term prediction of wind power, a new ultra-short-term prediction method for wind power is proposed by combining the maximal information coefficient (MIC) with multi-task learning (MTL) and long short-term memory (LSTM) network. First, the correlation analysis method is used to analyze the MIC correlation of wind power sequence and wind speed sequence, the MIC correlation between the alternative sequence, the wind power sequence and the wind speed sequence, respectively. The feature input sequence of the neural network is constructed base on the correlation analysis results. Second, taking wind speed prediction as the auxiliary task and wind power prediction as the main task, LSTM based prediction network was constructed using MTL framework, and the network parameters were optimized by grid search. Finally, based on the historical data of a wind farm in the United States, the case study verifies that the proposed method gains higher prediction accuracy than other existing methods modeling wind speed as a feature, such as single-task LSTM neural network, BP neural network and traditional ARIMA model.