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Wind power prediction is very important for the economic dispatching of power systems containing wind power. In this work, a novel short-term wind power prediction method based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) and (long short-term memory) LSTM neural network is proposed and studied. First, the original data is prepossessed including removing outliers and filling in the gaps. Then, the random forest algorithm is used to sort the importance of each meteorological factor and determine the input climate characteristics of the forecast model. In addition, this study conducts seasonal classification of the annual data where ICEEMDAN is adopted to divide the original wind power sequence into numerous modal components according to different seasons. On this basis, sample entropy is used to calculate the complexity of each component and reconstruct them into trend components, oscillation components, and random components. Then, these three components are input into the LSTM neural network, respectively. Combined with the predicted values of the three components, the overall power prediction results are obtained. The simulation shows that ICEEMDAN-SE-LSTM achieves higher prediction accuracy ranging from 1.57% to 9.46% than other traditional models, which indicates the reliability and effectiveness of the proposed method for power prediction.

Short-Term Wind Power Prediction Based on ICEEMDAN-SE-LSTM Neural Network Model with Classifying Seasonal