Effective energy consumption forecasting using empirical wavelet transform and long short-term memory

Lu Peng, Lin Wang, De Xia and Qinglu Gao

Energy, 2022, vol. 238, issue PB

Abstract: Energy consumption is an important issue of global concern. Accurate energy consumption forecasting can help balance energy demand and energy production. Although there are various energy consumption forecasting methods, the forecasting accuracy still needs to be improved. This study applied a long short-term memory-based model in energy consumption forecasting to achieve a better prediction performance and the more critical influencing factors are emphasized. Results of one comparative example and two extended applications show the proposed model achieves better prediction accuracy compared with basic long short-term memory and other existing popular models. Mean absolute percentage errors of the proposed model for three real-life cases are 4.01 %, 5.37 %, and 1.60 %, respectively. Therefore, the proposed model is a satisfactory method for energy consumption forecasting due to its high accuracy. The high-precision forecasting technology is important for the energy systems.

Keywords: Energy consumption forecasting; Long short-term memory; Empirical wavelet transform; Attention-based mechanism (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (43)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:238:y:2022:i:pb:s0360544221020041

DOI: 10.1016/j.energy.2021.121756

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