Multi-Step Forecasting for Hourly Wind Speed Using Multi-Attribute Decision Making with Empirical Mode Decomposition and Computational Intelligence Sensor

作者：Wang, Dongfeng^*; Wang, Fuqiang; Zhai, Yongjie; Niu, Chenglin

来源：Sensor Letters, 2013, 11(11): 2159-2163.

DOI：10.1166/sl.2013.2966

• Wind Speed Forecasting
• Empirical Mode Decomposition
• Multi-Attribute Decision
• Computational Intelligence

A multi-step forecasting method for wind speed, based on multi-attribute decision making (MADM) with empirical mode decomposition (EMD) and computational intelligence, is proposed. First, wind speed time series is decomposed into some different components using EMD theory. Second, four kinds of well trained computational intelligence algorithms, i.e., BP network, RBF network, ELMAN and support vector machine (SVM), are respectively used as forecasting models for each component. Third, subjective and objective combination weighting methods are considered together for MADM to meet deviation maximization criterion. Finally, EMD inverse transformation is used to obtain the final predicted wind speed. The prediction results show that the comprehensive forecasting model effectively reduces prediction error, and it has better generalization ability and higher precision than the above mentioned individual forecasting models.

• 出版日期2013-11
• 单位华北电力大学; 华北电力大学（保定）