Short-Term Daily Load Curve Forecasting Based on Fuzzy Information Granulation and Multi-Strategy Sensitivity
Li Bin1, Qin Fanglu1, Wu Yin2, Huang Jia1
1.Guangxi Key Laboratory of Power System Optimization and Energy Technology Guangxi University Nanning 530004 China; 2.Power Dispatch Control Center of Guangxi Power Grid Nanning 530023 China
Abstract：Due to the variable weather,the precision of short-term load forecasting curves and forecasting model can not fit the demand of power grid.This paper proposes a short-term daily load curve forecasting method based on fuzzy information granulation and multi-strategy sensitivity.The concept of global meteorology factor is introduced,and used to build up the meteorological granulating set.Spatial multiple regression and lag model combined with multi-strategy sensitivity analysis method is applied to establish the peak load prediction model in very complex situation.On the basis of modified k-means cluster method,the meteorology characteristic day is grasped,and then preliminary prediction curve is got.The paper judges the deformation probability intelligently and takes optimized correction necessarily;uses daily dynamic data flow to update the modelling parameters to forecast precisely.The proposed method is verified through an application to annual load data of the southern China area,the high accuracy proves its practicability,especially fitting for the complicated and variable weather condition in short time.
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2017, Vol. 32 
