Shumin Sun1, Peng Yu1, Jiawei Xing1, Yan Cheng1, Song Yang1, Qian Ai2,*
Energy Engineering, Vol.120, No.12, pp. 2761-2782, 2023, DOI:10.32604/ee.2023.042635
Abstract 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… More >
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