Short-term Prediction of Wind Speed for Wind Farm Based on IDE-LSSVM Model

doi:10.16182/j.issn1004731x.joss.201707022

Abstract

Abstract: To improve the prediction accuracy of short-term wind speed for wind farm, an improved differential evolution algorithm was applied to optimize the parameters of least squares support vector machine. Two mutation operators were integrated, and the scale factor and crossover probability factor were changed gradually to adapt to the evolutionary generations. The good global search ability and population diversity in early stage of evolution were ensured, therefore the local search accuracy and the convergence speed in the late stage were enhanced. The forecasting performance of the least squares support vector machine optimized by IDE was improved. Simulation experiments on the historical wind speed data sets in a wind farm of Hebei province show that the proposed model is effective.

Key words: wind farm, short-term wind speed prediction, least squares support vector machine, improved differential evolution algorithm

CLC Number:

TM614

Zhang Yan, Wang Dongfeng, Han Pu. Short-term Prediction of Wind Speed for Wind Farm Based on IDE-LSSVM Model[J]. Journal of System Simulation, 2017, 29(7): 1561-1571.

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