A Weekend Load Forecasting Model Based on Semi-Parametric Regression Analysis Considering Weather and Load Interaction

Bin Li, Mingzhen Lu, Yiyi Zhang and Jia Huang
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Bin Li: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Mingzhen Lu: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Yiyi Zhang: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China
Jia Huang: Guangxi Key Laboratory of Power System Optimization and Energy Technology, Guangxi University, Nanning 530004, China

Energies, 2019, vol. 12, issue 20, 1-19

Abstract: Compared to the load characteristics of normal working days, weekend load characteristics have a low level of load and are sensitive to meteorological conditions, which influences the accuracy of short-term weekend-load forecasting. To solve this problem and to improve the accuracy of short-term weekend-load forecasting, a Semi-parametric weekend-load forecasting method based on the interaction between meteorological and load is proposed in this paper. The main work is shown as follows: (1) through separating weekend-load from normal-load and analyzing the correlation between meteorological factors and daily maximum load, the meteorological factors with parameter characteristics and non-parameter characteristics can be screened out; (2) a short-term weekend-load forecasting model is built according to Semi-parametric regression theory which can express the coupling relation between meteorology and load more realistically; (3) the effect of temperature accumulation is also considered to correct the forecasting model. The proposed method is proved by implementing short-term weekend-load forecasting on the real historical data of the Southern Power Grid in China. The result shows that the 96-point mean load forecasting accuracy obtained by this model can meet the requirement of power network operation.

Keywords: weekend load forecasting; meteorological information; Semi-parametric regression theory; agglomeration effect (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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