Title :
Short-term load forecasting using multiple support vector machines based on fuzzy clustering
Author :
Gaorong ; Xiao-hua, Liu
Author_Institution :
Shool of Math. & Inf., LuDong Univ., Yantai, China
Abstract :
According to the future of power load, a load forecasting method of multiple support vector machine based on fuzzy clustering is proposed. Data type, weather and temperature factors are considered in the model. Load data are classified using fuzzy clustering. Each class was modeled using support vector machines which best fitted the special class. The method was simulated utilizing the load data of Shan Dong electrical company from 2005 to 2007. The simulation result showed our method can improve the forecasting accuracy.
Keywords :
environmental factors; fuzzy set theory; load forecasting; power engineering computing; support vector machines; Shan Dong electrical company; data type; fuzzy clustering; multiple support vector machines; short-term load forecasting; temperature factors; weather factors; Load forecasting; Mathematics; Predictive models; Risk management; Support vector machine classification; Support vector machines; Temperature; Weather forecasting; fuzzy clustering; short-term load forecasting; support vector machines;
Conference_Titel :
Control and Decision Conference, 2009. CCDC '09. Chinese
Conference_Location :
Guilin
Print_ISBN :
978-1-4244-2722-2
Electronic_ISBN :
978-1-4244-2723-9
DOI :
10.1109/CCDC.2009.5192735
