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	Hybrid Short-term Load Forecasting Using Principal Component Analysis and MEA-Elman Network
	Bao, Guangqing2; Lin, Qilin2; Gong, Dunwei3; Shao, Huixing1
	2016
关键词	Meteorological factor PCA Mind Evolutionary Algorithm Optimization The Elman network Short-term load forecasting
卷号	9773
DOI	10.1007/978-3-319-42297-8_62
页码	671-683
英文摘要	Meteorological factors, the main causes that impact the power load, have become a research focus on load forecasting in recent years. In order to represent the influence of weather factors on the power load comprehensively and succinctly, this paper uses PCA to reduce the dimension of multi-weather factors and get comprehensive variables. Besides, in view of a relatively low dynamic performance of BP network, a model for short-term load forecasting based on Elman network is presented. When adopting the BP algorithm, Elman network has such problems as being apt to fall into local optima, many iterations and low efficiency. To overcome these drawbacks, this paper improves the active function, optimizes its weights and thresholds using MEA, and formulates a MEA-Elman model to forecast the power load. An example of load forecasting is provided, and the results indicate that the proposed method can improve the accuracy and the efficiency.
会议录出版者	SPRINGER INT PUBLISHING AG
会议录出版地	GEWERBESTRASSE 11, CHAM, CH-6330, SWITZERLAND
语种	英语
WOS研究方向	Computer Science
WOS记录号	WOS:000387430500062
内容类型	会议论文
源URL	[http://119.78.100.223/handle/2XXMBERH/36434]
专题	电气工程与信息工程学院
作者单位	1.State Grid Huangshan Power Supply Co, Huangshan 245000, Peoples R China 2.Lanzhou Univ Technol, Coll Elect Engn & Informat Engn, Lanzhou 730050, Peoples R China; 3.China Univ Min & Technol, Sch Informat & Elect Engn, Xuzhou 221008, Peoples R China;
推荐引用方式 GB/T 7714	Bao, Guangqing,Lin, Qilin,Gong, Dunwei,et al. Hybrid Short-term Load Forecasting Using Principal Component Analysis and MEA-Elman Network[C]. 见:.

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