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

Author

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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)

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.

Suggested Citation

  • Yu Jin & Honggang Guo & Jianzhou Wang & Aiyi Song, 2020. "A Hybrid System Based on LSTM for Short-Term Power Load Forecasting," Energies, MDPI, vol. 13(23), pages 1-32, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6241-:d:451845
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    References listed on IDEAS

    as
    1. Kavasseri, Rajesh G. & Seetharaman, Krithika, 2009. "Day-ahead wind speed forecasting using f-ARIMA models," Renewable Energy, Elsevier, vol. 34(5), pages 1388-1393.
    2. Guo‐Feng Fan & Yan‐Hui Guo & Jia‐Mei Zheng & Wei‐Chiang Hong, 2020. "A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 39(5), pages 737-756, August.
    3. Wang, Jian Qi & Du, Yu & Wang, Jing, 2020. "LSTM based long-term energy consumption prediction with periodicity," Energy, Elsevier, vol. 197(C).
    4. Mohamed Massaoudi & Shady S. Refaat & Haitham Abu-Rub & Ines Chihi & Fakhreddine S. Oueslati, 2020. "PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting," Energies, MDPI, vol. 13(20), pages 1-29, October.
    5. Yang, Wendong & Wang, Jianzhou & Niu, Tong & Du, Pei, 2019. "A hybrid forecasting system based on a dual decomposition strategy and multi-objective optimization for electricity price forecasting," Applied Energy, Elsevier, vol. 235(C), pages 1205-1225.
    6. He, Qingqing & Wang, Jianzhou & Lu, Haiyan, 2018. "A hybrid system for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 226(C), pages 756-771.
    7. Song, Jingjing & Wang, Jianzhou & Lu, Haiyan, 2018. "A novel combined model based on advanced optimization algorithm for short-term wind speed forecasting," Applied Energy, Elsevier, vol. 215(C), pages 643-658.
    8. Zhang, Yagang & Pan, Guifang & Chen, Bing & Han, Jingyi & Zhao, Yuan & Zhang, Chenhong, 2020. "Short-term wind speed prediction model based on GA-ANN improved by VMD," Renewable Energy, Elsevier, vol. 156(C), pages 1373-1388.
    9. Guo, Zhifeng & Zhou, Kaile & Zhang, Xiaoling & Yang, Shanlin, 2018. "A deep learning model for short-term power load and probability density forecasting," Energy, Elsevier, vol. 160(C), pages 1186-1200.
    10. Zhang, Xiaobo & Wang, Jianzhou & Gao, Yuyang, 2019. "A hybrid short-term electricity price forecasting framework: Cuckoo search-based feature selection with singular spectrum analysis and SVM," Energy Economics, Elsevier, vol. 81(C), pages 899-913.
    11. Hongze Li & Hongyu Liu & Hongyan Ji & Shiying Zhang & Pengfei Li, 2020. "Ultra-Short-Term Load Demand Forecast Model Framework Based on Deep Learning," Energies, MDPI, vol. 13(18), pages 1-16, September.
    12. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
    13. Jingyi Liu & Xinxin Liu & Ba Tuan Le, 2019. "Rolling Force Prediction of Hot Rolling Based on GA-MELM," Complexity, Hindawi, vol. 2019, pages 1-11, June.
    14. Seunghyoung Ryu & Jaekoo Noh & Hongseok Kim, 2016. "Deep Neural Network Based Demand Side Short Term Load Forecasting," Energies, MDPI, vol. 10(1), pages 1-20, December.
    15. Wang, Jianzhou & Wang, Shiqi & Yang, Wendong, 2019. "A novel non-linear combination system for short-term wind speed forecast," Renewable Energy, Elsevier, vol. 143(C), pages 1172-1192.
    16. Mustafa Jaihuni & Jayanta Kumar Basak & Fawad Khan & Frank Gyan Okyere & Elanchezhian Arulmozhi & Anil Bhujel & Jihoon Park & Lee Deog Hyun & Hyeon Tae Kim, 2020. "A Partially Amended Hybrid Bi-GRU—ARIMA Model (PAHM) for Predicting Solar Irradiance in Short and Very-Short Terms," Energies, MDPI, vol. 13(2), pages 1-20, January.
    Full references (including those not matched with items on IDEAS)

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    Cited by:

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    8. Yamin Shen & Yuxuan Ma & Simin Deng & Chiou-Jye Huang & Ping-Huan Kuo, 2021. "An Ensemble Model based on Deep Learning and Data Preprocessing for Short-Term Electrical Load Forecasting," Sustainability, MDPI, vol. 13(4), pages 1-21, February.
    9. Jingming Su & Xuguang Han & Yan Hong, 2023. "Short Term Power Load Forecasting Based on PSVMD-CGA Model," Sustainability, MDPI, vol. 15(4), pages 1-23, February.
    10. Wang, Jianzhou & Xing, Qianyi & Zeng, Bo & Zhao, Weigang, 2022. "An ensemble forecasting system for short-term power load based on multi-objective optimizer and fuzzy granulation," Applied Energy, Elsevier, vol. 327(C).
    11. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
    12. Taorong Jia & Lixiao Yao & Guoqing Yang & Qi He, 2022. "A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU," Sustainability, MDPI, vol. 14(24), pages 1-18, December.
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    14. Miguel A. Jaramillo-Morán & Daniel Fernández-Martínez & Agustín García-García & Diego Carmona-Fernández, 2021. "Improving Artificial Intelligence Forecasting Models Performance with Data Preprocessing: European Union Allowance Prices Case Study," Energies, MDPI, vol. 14(23), pages 1-23, November.
    15. Sizhe Zhang & Jinqi Liu & Jihong Wang, 2023. "High-Resolution Load Forecasting on Multiple Time Scales Using Long Short-Term Memory and Support Vector Machine," Energies, MDPI, vol. 16(4), pages 1-22, February.
    16. Vasileios Laitsos & Georgios Vontzos & Dimitrios Bargiotas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2023. "Enhanced Automated Deep Learning Application for Short-Term Load Forecasting," Mathematics, MDPI, vol. 11(13), pages 1-21, June.
    17. Hasnain Iftikhar & Josue E. Turpo-Chaparro & Paulo Canas Rodrigues & Javier Linkolk López-Gonzales, 2023. "Day-Ahead Electricity Demand Forecasting Using a Novel Decomposition Combination Method," Energies, MDPI, vol. 16(18), pages 1-22, September.
    18. Guo, Honggang & Wang, Jianzhou & Li, Zhiwu & Jin, Yu, 2022. "A multivariable hybrid prediction system of wind power based on outlier test and innovative multi-objective optimization," Energy, Elsevier, vol. 239(PE).

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