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An intelligent method for wind power forecasting based on integrated power slope events prediction and wind speed forecasting
Author(s) -
Li Fudong,
Liao Huanyu
Publication year - 2018
Publication title -
ieej transactions on electrical and electronic engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.254
H-Index - 30
eISSN - 1931-4981
pISSN - 1931-4973
DOI - 10.1002/tee.22671
Subject(s) - wind speed , wind power , artificial neural network , wind power forecasting , time series , computer science , power (physics) , electric power system , engineering , meteorology , artificial intelligence , machine learning , physics , quantum mechanics , electrical engineering
In this paper, we study an intelligent wind power prediction method by taking the prediction time horizons and prediction accuracy into account. The wind power slope events are defined, and multiple support vector machines are applied to the classification of slope down/up events for multistep‐ahead scenarios. The wind speed series are decomposed by using the maximum overlap discrete wavelet transform (MODWT), and each decomposed signal is forecast using an adaptive wavelet neural network (AWNN) individually. The network is trained for wind speed prediction up to 24 h ahead. Based on slope events forecasting and wind speed forecasting, an improved radial basis function neural network (RBFNN) is proposed to predict wind power up to 24 h ahead. The proposed model is tested by using wind power data collected from a real wind farm. The analysis results validate that both the prediction time horizons and the prediction accuracy are guaranteed, and the proposed method can be applied to the optimal scheduling of wind farms 1 day in advance. © 2018 Institute of Electrical Engineers of Japan. Published by John Wiley & Sons, Inc.