Ultra Short-Term Power Load Forecasting Based on Similar Day Clustering and Ensemble Empirical Mode Decomposition

Wenhui Zeng, Jiarui Li, Changchun Sun, Lin Cao, Xiaoping Tang (), Shaolong Shu and Junsheng Zheng
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Wenhui Zeng: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Jiarui Li: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Changchun Sun: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Lin Cao: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Xiaoping Tang: Huadian Electric Power Research Institute Co., Ltd., Hangzhou 310030, China
Shaolong Shu: School of Electronics and Information Engineering, Tongji University, Shanghai 201804, China
Junsheng Zheng: School of Automotive Studies, Tongji University, Shanghai 201804, China

Energies, 2023, vol. 16, issue 4, 1-15

Abstract: With the increasing demand of the power industry for load forecasting, improving the accuracy of power load forecasting has become increasingly important. In this paper, we propose an ultra short-term power load forecasting method based on similar day clustering and EEMD (Ensemble Empirical Mode Decomposition). In detail, the K-means clustering algorithm was utilized to divide the historical data into different clusters. Through EEMD, the load data of each cluster were decomposed into several sub-sequences with different time scales. The LSTNet (Long- and Short-term Time-series Network) was adopted as the load forecasting model for these sub-sequences. The forecast results for different sub-sequences were combined as the expected result. The proposed method predicts the load in the next 4 h with an interval of 15 min. The experimental results show that the proposed method obtains higher prediction accuracy than other comparable forecasting models.

Keywords: cluster analysis; mode decomposition; LSTNet; ultra short-term load forecasting; non-stationary time series (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: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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