Wind speed prediction using reduced support vector machines with feature selection

Accurate prediction of wind speed is one of the most effective ways to solve the problems of relaibility, security, stability and quality, which are caused by wind energy production in power systems. This paper presents a wind speed prediction concept with high efficiency convex optimization support vector machine for data regression (SVR). Based on the SVR, a reduced support vector machine (RSVM) is proposed, which preselects a subset of data as support vectors and solves a smaller optimization problem. The principal component analysis is utilized to determine the outcome of the major factors affecting the wind speed. With increasing number of the input variables in RSVM for regression structure, particle swarm optimization (PSO) is incorporated to optimize the parameters. Detailed analysis and simulations using the real time wind power plant data demonstrate the effectiveness of the RSVM-based forecasting approach. (C) 2015 Elsevier B.V. All rights reserved.