Title :
Short-term load forecasting based on least square support vector machine combined with fuzzy control
Author :
Gao, Rong ; Zhang, Liyuan ; Liu, Xiaohua
Author_Institution :
Shool of Math. & Inf., LuDong Univ., Yantai, China
Abstract :
A short-term load forecasting method based on least square support vector machine(LS-SVM) combined with fuzzy control was proposed. The peak load and valley load was forecasted by LS-SVM model which was built by analysis of load data and meteorological data. Then the peak load and valley load was tuned by fuzzy rules which has been built by forecasting error data. One day and one week ahead load has been got by combing peak load and valley load with similar day load change coefficient. The load data and meteorological data of Shan Dong electrical company of 2008 was utilized to test the forecasting model. The simulation result shows the proposed method can improve the predicting accuracy.
Keywords :
fuzzy control; least squares approximations; load flow control; load forecasting; support vector machines; LS-SVM model; forecasting error; fuzzy control; fuzzy rule; least square support vector machine; load change coefficient; load data analysis; load forecasting method; peak load; valley load; Data models; Forecasting; Fuzzy control; Load forecasting; Load modeling; Predictive models; Support vector machines; fuzzy control; least square support vector machine; power system; short-term load forecasting;
Conference_Titel :
Intelligent Control and Automation (WCICA), 2012 10th World Congress on
Conference_Location :
Beijing
Print_ISBN :
978-1-4673-1397-1
DOI :
10.1109/WCICA.2012.6358034
