Short term forecast of wind power generation based on SVM with pattern matching

Abstract

This paper investigates short term forecast of wind power generation using Support Vector Machine (SVM) methods. We propose a similar pattern matching technique for data pre-processing. The proposed technique is able to select the most appropriate segments from the available historical data for the forecasting. These segments are used for the training of parameters in the SVM model, which will then be able to provide the desired forecast. We study the reliability of the technique in wind speed forecasting and power generation forecasting. The proposed technique is evaluated by using the public data sets obtained from the National Renewable Energy Laboratory.