Short-term load forecasting based on support vector machines regression

	Short-term load forecasting based on support vector machines regression
	Zhang, MG
	2005
关键词	support vector machines(SVM) short-term load forecasting(STLF) structural risk minimization (SRM) BP neural network
页码	4310-4314
英文摘要	A novel method based on SVM for the electric power system short-term load forecasting was presented. The proposed algorithm embodies the Structural Risk Minimization (SRM) principle is more generalized performance and accurate as compared to artificial neural network which embodies the Embodies Risk Minimization (ERM) principle. The theory of the SVM algorithm is based on statistical learning theory. Training of SVM leads to a quadratic programming problem. In order to improve forecast accuracy, the SVM interpolates among the load and temperature data in a training data set. Analysis of the experimental results proved that SVM could achieve greater accuracy and faster speed than the BP neural network.
会议录	Proceedings of 2005 International Conference on Machine Learning and Cybernetics, Vols 1-9
会议录出版者	IEEE
会议录出版地	345 E 47TH ST, NEW YORK, NY 10017 USA
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000235325606074
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/38318]
专题	电气工程与信息工程学院
通讯作者	Zhang, MG
作者单位	Lanzhou Univ Technol, Sch Elect & Informat Engn, Lanzhou 730050, Peoples R China
推荐引用方式 GB/T 7714	Zhang, MG. Short-term load forecasting based on support vector machines regression[C]. 见:.