An Empirical Mode Decomposition-Based Hybrid Model for Sub-Hourly Load Forecasting

Chuang Yin, Nan Wei (), Jinghang Wu, Chuhong Ruan, Xi Luo and Fanhua Zeng ()
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Chuang Yin: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Nan Wei: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Jinghang Wu: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Chuhong Ruan: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Xi Luo: Cyberspace Institute of Advanced Technology, Guangzhou University, Guangzhou 510006, China
Fanhua Zeng: Faculty of Engineering and Applied Science, University of Regina, Regina, SK S4S 0A2, Canada

Energies, 2024, vol. 17, issue 2, 1-17

Abstract: Sub-hourly load forecasting can provide accurate short-term load forecasts, which is important for ensuring a secure operation and minimizing operating costs. Decomposition algorithms are suitable for extracting sub-series and improving forecasts in the context of short-term load forecasting. However, some existing algorithms like singular spectrum analysis (SSA) struggle to decompose high sampling frequencies and rapidly changing sub-hourly load series due to inherent flaws. Considering this, we propose an empirical mode decomposition-based hybrid model, named EMDHM. The decomposition part of this novel model first detrends the linear and periodic components from the original series. The remaining detrended long-range correlation series is simplified using empirical mode decomposition (EMD), generating intrinsic mode functions (IMFs). Fluctuation analysis is employed to identify high-frequency information, which divide IMFs into two types of long-range series. In the forecasting part, linear and periodic components are predicted by linear and trigonometric functions, while two long-range components are fitted by long short-term memory (LSTM) for prediction. Four forecasting series are ensembled to find the final result of EMDHM. In experiments, the model’s framework we propose is highly suitable for handling sub-hourly load datasets. The MAE, RMSE, MARNE, and R 2 of EMDHM have improved by 20.1%, 26.8%, 22.1%, and 5.4% compared to single LSTM, respectively. Furthermore, EMDHM can handle both short- and long-sequence, sub-hourly load forecasting tasks. Its R 2 only decreases by 4.7% when the prediction length varies from 48 to 720, which is significantly lower than other models.

Keywords: sub-hourly load forecasting; empirical mode decomposition; hybrid model; fluctuation analysis; high-frequency 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: 2024
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