基于SVM与相似日的短期电力负荷预测

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Abstract As to the short-term electric power load forecasting, its accuracy is affected by many uncertain influence factors. To solve this problem, a novel method using Similar Days based on fuzzy clustering analysis was proposed in this paper. Firstly it categorized weather factors as temperature, air pressure, wind speed, overcast and rainy etc, then together with week type and day type they consist of the influence items. According to the items above, fuzzy rules were applied to establish the mapping table to get the factors quantized. Next cluster technology was utilized to classify the content in the mapping table, and the similar days are chosen out based on the clustering level, which is to reduce the numbers of samples and accelerate the speed of selection. Secondly aiming to eliminating non-gaussian noise contained in the similar day’s power load, wavelet transform was adopted to extract the low sequence components. Finally a SVM(Support Vector Machine), which was optimized by PSO(particle swarm optimization)algorithm, was designed to predict the low frequency part, while, mean square weighted method was used to predict the high frequency part. The simulation results show this approach is encouraging.

Key words： keywords：Short-term power load forecasting fuzzy clustering analysis similar days wavelet transform particle swarm optimization support vector machine

Received: 20 September 2012 Published: 12 December 2013

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	Yu Long
	Zheng Yihui
	Wang Xin
	Li Lixue
	Zhou Lidan
	Chen Hongtao

Cite this article:

Yu Long,Zheng Yihui,Wang Xin等. Short Term Load Forecasting Based on SVM and Similar Days[J]. Transactions of China Electrotechnical Society, 2013, 28(1增): 217-223.

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http://dgjsxb.ces-transaction.com/EN/ OR http://dgjsxb.ces-transaction.com/EN/Y2013/V28/I1增/217

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