基于模糊信息粒化与多策略灵敏度的短期日负荷曲线预测

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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.

Key words： Short-term load forecasting(STLF) global meteorology factor information granulated spatial multiple regression and sensitivity analysis modified K-means cluster

Received: 27 March 2016 Published: 12 May 2017

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TM714

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	Li Bin
	Qin Fanglu
	Wu Yin
	Huang Jia

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Li Bin,Qin Fanglu,Wu Yin等. Short-Term Daily Load Curve Forecasting Based on Fuzzy Information Granulation and Multi-Strategy Sensitivity[J]. Transactions of China Electrotechnical Society, 2017, 32(9): 149-159.

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https://dgjsxb.ces-transaction.com/EN/ OR https://dgjsxb.ces-transaction.com/EN/Y2017/V32/I9/149

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