Multi-model Fuzzy Synthesis Forecasting of Electric Power Loads for Larger Consumers
Gu Yundong1, Zhang Sujie1, Feng Junshu2
1.School of Mathematics and Physics North China Electric Power University Beijing 102206 China 2.School of Electrical and Electronic Engineering North China Electric Power University Beijing 102206 China
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.
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2015, Vol. 30 
