大用户电力负荷的多模型模糊综合预测

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Abstract A multi-model based variable weighted fuzzy synthesis forecasting method is proposed for the power load forecasting of large consumers.A clustering algorithm based on Renyi-entropy and centroid similarity is introduced to mining typical load patterns from historical load data and grouping them according to similarities as well as detecting atypical outliers.A conjugate gradient based learning algorithm for the RBF neural network is designed to construct unit forecasting model for each group of typical load patterns.Then,the forecasting results of all unit models are integrated adaptively by using variable weighted fuzzy synthesis inference.The simulation results show that the multi-model fuzzy synthesis forecasting method can raise the prediction accuracy and stability significantly.

Key words： Electric power load forecasting for large consumers centroid similarity based clustering RBF neural network multi-model fuzzy synthesis forecasting fuzzy reasoning

Received: 05 January 2015 Published: 18 December 2015

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TM76

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	Articles by authors
	Gu Yundong
	Zhang Sujie
	Feng Junshu

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Gu Yundong,Zhang Sujie,Feng Junshu. Multi-model Fuzzy Synthesis Forecasting of Electric Power Loads for Larger Consumers[J]. Transactions of China Electrotechnical Society, 2015, 30(23): 110-115.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2015/V30/I23/110

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