A Hybrid System Based on LSTM for Short-Term Power Load Forecasting

Yu Jin, Honggang Guo, Jianzhou Wang and Aiyi Song
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Yu Jin: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Honggang Guo: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Jianzhou Wang: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
Aiyi Song: School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China

Energies, 2020, vol. 13, issue 23, 1-32

Abstract: As the basic guarantee for the reliability and economic operations of state grid corporations, power load prediction plays a vital role in power system management. To achieve the highest possible prediction accuracy, many scholars have been committed to building reliable load forecasting models. However, most studies ignore the necessity and importance of data preprocessing strategies, which may lead to poor prediction performance. Thus, to overcome the limitations in previous studies and further strengthen prediction performance, a novel short-term power load prediction system, VMD-BEGA-LSTM (VLG), integrating a data pretreatment strategy, advanced optimization technique, and deep learning structure, is developed in this paper. The prediction capability of the new system is evaluated through simulation experiments that employ the real power data of Queensland, New South Wales, and South Australia. The experimental results indicate that the developed system is significantly better than other comparative systems and shows excellent application potential.

Keywords: power load forecasting; hybrid analysis-forecast system; data preprocessing strategy; deep learning structure; optimization 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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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