Support vector regression based on grid-search method for short-term wind power forecasting

• Main Authors: Zhang, Hong, Chen, Lixing, Qu, Yong, Zhao, Guo, Guo, Zhenwei
• Other Authors: School of Electrical and Electronic Engineering
• Format: Article
• Language: English
• Published: 2018
• Subjects: Wind Power Forecasting; Support Vector Regression (SVR); DRNTU::Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/87598; http://hdl.handle.net/10220/46771
• Institution: Nanyang Technological University

The purpose of this paper is to investigate the short-term wind power forecasting. STWPF is a typically complex issue, because it is affected by many factors such as wind speed, wind direction, and humidity. This paper attempts to provide a reference strategy for STWPF and to solve the problems in existence. The two main contributions of this paper are as follows. (1) In data preprocessing, each encountered problem of employed real data such as irrelevant, outliers, missing value, and noisy data has been taken into account, the corresponding reasonable processing has been given, and the input variable selection and order estimation are investigated by Partial least squares technique. (2) STWPF is investigated by multiscale support vector regression (SVR) technique, and the parameters associated with SVR are optimized based on Grid-search method. In order to investigate the performance of proposed strategy, forecasting results comparison between two different forecasting models, multiscale SVR and multilayer perceptron neural network applied for power forecasts, are presented. In addition, the error evaluation demonstrates that the multiscale SVR is a robust, precise, and effective approach.