Open AccessHybrid forecasting model‐based data mining and genetic algorithm‐adaptive particle swarm optimisation: a case study of wind speed time series
Author(s) -
Wang Jianzhou,
Zhang Fanyong,
Liu Feng,
Ma Jianjun
Publication year - 2016
Publication title -
iet renewable power generation
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.005
H-Index - 76
ISSN - 1752-1424
DOI - 10.1049/iet-rpg.2015.0010
Subject(s) - particle swarm optimization , wind speed , computer science , artificial neural network , data pre processing , wind power , genetic algorithm , time series , renewable energy , data mining , support vector machine , algorithm , artificial intelligence , machine learning , engineering , meteorology , physics , electrical engineering
Wind energy has been part of the fastest growing renewable energy sources and is clean and pollution‐free. Wind energy has been gaining increasing global attention, and wind speed forecasting plays a vital role in the wind energy field. However, such forecasting has been demonstrated to be a challenging task due to the effect of various meteorological factors. This study proposes a hybrid forecasting model that can effectively provide preprocessing for the original data and improve forecasting accuracy. The developed model applies a genetic algorithm‐adaptive particle swarm optimisation algorithm to optimise the parameters of the wavelet neural network (WNN) model. The proposed hybrid method is subsequently examined in regard to the wind farms of eastern China. The forecasting performance demonstrates that the developed model is better than some traditional models (for example, back propagation, WNN, fuzzy neural network, and support vector machine), and its applicability is further verified by the paired‐sample T tests.