Forecasting Hourly Power Load Considering Time Division: A Hybrid Model Based on K-means Clustering and Probability Density Forecasting Techniques

Fuqiang Li, Shiying Zhang, Wenxuan Li, Wei Zhao, Bingkang Li and Huiru Zhao
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Fuqiang Li: North China Branch of State Grid Corporation of China, Beijing 100053, China
Shiying Zhang: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Wenxuan Li: North China Branch of State Grid Corporation of China, Beijing 100053, China
Wei Zhao: North China Branch of State Grid Corporation of China, Beijing 100053, China
Bingkang Li: School of Economics and Management, North China Electric Power University, Beijing 102206, China
Huiru Zhao: School of Economics and Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2019, vol. 11, issue 24, 1-17

Abstract: In comparison with traditional point forecasting method, probability density forecasting can reflect the load fluctuation more effectively and provides more information. This paper proposes a hybrid hourly power load forecasting model, which integrates K-means clustering algorithm, Salp Swarm Algorithm (SSA), Least Square Support Vector Machine (LSSVM), and kernel density estimation (KDE) method. Firstly, the loads at 24 times a day are grouped into three categories according to the K-means clustering algorithm, which correspond to the valley period, flat period, and peak period of the load, respectively. Secondly, the load point forecasting value is obtained by LSSVM method optimized by SSA algorithm. Furthermore, the kernel density estimation method is employed to fit the forecasting error of SSA-LSSVM in different time periods, and the probability density function of the error distribution is obtained. The final load probability density forecasting result is obtained by combining the point forecasting value and the error fitting result, and then the upper and lower limits of the confidence interval under the given confidence level are solved. In this paper, the performance of the model is evaluated by two indicators named interval coverage and interval average width. Meanwhile, in comparison with several other models, it can be concluded that the proposed model can effectively improve the forecasting effect.

Keywords: hourly load forecasting; time division; k-means clustering; SSA-LSSVM technique; kernel density estimation (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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