Hadoop架构下基于模式匹配的短期电力负荷预测方法

doi:10.19595/j.cnki.1000-6753.tces.170121

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Abstract Big data processing technologies make full use of massive dynamic data, which can improve the accuracy and efficiency of power load forecasting. To this end, realize the short-term load forecasting based on load pattern matching combined with Hadoop framework. Typical day-load patterns are deduced from the similarity of the daily load sequences, and the decision trees are built by identifying the important influence factors based on the parallel random forest to achieve the fast and accurate matching of the daily load pattern. The forecasting model of multiple time point for each typical load pattern is constructed in MapReduce computing framework for load prediction and analysis to get the prediction results of the following day load using a large number of samples. The simulation analysis used the whole-year load data in a city power grid and compared with locally weighted linear regression (LWLR) algorithm by the mean error, root mean square error and other indices. The results show that the method can provide higher prediction accuracy and computational efficiencyin short-term load forecasting.

Key words： Hadoop framework load forecasting influence factor decision trees pattern matching

Received: 08 February 2017 Published: 12 April 2018

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TM714

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	Articles by authors
	Wu Runze
	Bao Zhengrui
	Wang Wentao
	Deng Wei
	Tang Liangrui

Cite this article:

Wu Runze,Bao Zhengrui,Wang Wentao等. Short-Term Power Load Forecasting Method Based on Pattern Matching in Hadoop Framework[J]. Transactions of China Electrotechnical Society, 2018, 33(7): 1542-1551.

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https://dgjsxb.ces-transaction.com/EN/10.19595/j.cnki.1000-6753.tces.170121 OR https://dgjsxb.ces-transaction.com/EN/Y2018/V33/I7/1542

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