Short-term load forecasting using SVR (support vector regression)-based radial basis function neural network with dual extended Kalman filter

• Published in: Energy (Oxford) 2013-01, Vol.49 (1), p.413-422
• Main Authors: Ko, Chia-Nan, Lee, Cheng-Ming
• Format: Article
• Language: English
• Subjects: Algorithms; Applied sciences; Capacitors. Resistors. Filters; Computer simulation; Dual extended Kalamn filter; Electrical engineering. Electrical power engineering; Electrical power engineering; Energy; Energy. Thermal use of fuels; Exact sciences and technology; Forecasting; Mathematical analysis; Mathematical models; Neural networks; Operation. Load control. Reliability; Power networks and lines; prediction; Radial basis function; Radial basis function neural network; Short-term load forecasting; Support vector regression; Various equipment and components; Vectors (mathematics)
• ISSN: 0360-5442
• DOI: 10.1016/j.energy.2012.11.015

Accurate load forecasting is an important issue for the reliable and efficient operation of the power system. This paper presents a hybrid algorithm which combines SVR (support vector regression), RBFNN (radial basis function neural network), and DEKF (dual extended Kalamn filter) to construct a prediction model (SVR–DEKF–RBFNN) for short-term load forecasting. In the proposed model, first, the SVR model is employed to determine both the structure and initial parameters of the RBFNN. After initialization, the DEKF is used as the learning algorithm to optimize the parameters of the RBFNN. Finally, the optimal RBFNN model is adopted to predict short-term load. The performance of the proposed approach is evaluated on real-load data from the Taipower Company, and compared with DEKF–RBFNN and GRD-RBFNN (gradient decent RBFNN) models. Simulation results of three cases show that the proposed method has better forecasting performance than the other methods. ► Propose a prediction model integrating SVR, RBFNN, and DEKF for short-term load forecasting. ► SVR model is employed to determine both the structure and initial parameters of the RBFNN. ► DEKF is used as the learning algorithm to optimize the parameters of the RBFNN. ► Illustrate the performance of the proposed SVR–DEKF–RBFNN for predicting real-load data. ► Compare the simulation results with DEKF–RBFNN and GRD-RBFNN models.