Short-Term Electric Load Forecasting Based on Signal Decomposition and Improved TCN Algorithm

Xinjian Xiang (), Tianshun Yuan, Guangke Cao and Yongping Zheng
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Xinjian Xiang: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Tianshun Yuan: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China
Guangke Cao: Key Laboratory of Intelligent Operation and Maintenance Robot of Zhejiang Province, Hangzhou Shenhao Technology, Hangzhou 311121, China
Yongping Zheng: School of Automation and Electrical Engineering, Zhejiang University of Science and Technology, Hangzhou 310023, China

Energies, 2024, vol. 17, issue 8, 1-21

Abstract: In the realm of power systems, short-term electric load forecasting is pivotal for ensuring supply–demand balance, optimizing generation planning, reducing operational costs, and maintaining grid stability. Short-term load curves are characteristically coarse, revealing high-frequency data upon decomposition that exhibit pronounced non-linearity and significant noise, complicating efforts to enhance forecasting precision. To address these challenges, this study introduces an innovative model. This model employs complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) to bifurcate the original load data into low- and high-frequency components. For the smoother low-frequency data, a temporal convolutional network (TCN) is utilized, whereas the high-frequency components, which encapsulate detailed load history information yet suffer from a lower fitting accuracy, are processed using an enhanced soft thresholding TCN (SF-TCN) optimized with the slime mould algorithm (SMA). Experimental tests of this methodology on load forecasts for the forthcoming 24 h across all seasons have demonstrated its superior forecasting accuracy compared to that of non-decomposed models, such as support vector regression (SVR), recurrent neural network (RNN), gated recurrent unit (GRU), long short-term memory (LSTM), convolutional neural network-LSTM (CNN-LSTM), TCN, Informer, and decomposed models, including CEEMDAN-TCN and CEEMDAN-TCN-SMA.

Keywords: electric load forecasting; forecasting; complete ensemble empirical mode decomposition with adaptive noise; temporal convolutional network; soft thresholding temporal convolutional network; slime mould algorithm (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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