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Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM

Author

Listed:
  • Jieyun Zheng

    (Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China)

  • Linyao Zhang

    (Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China)

  • Jinpeng Chen

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China)

  • Guilian Wu

    (Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China)

  • Shiyuan Ni

    (Economic Technology Research Institute, State Grid Fujian Electric Power Co., Ltd., Fuzhou 350000, China)

  • Zhijian Hu

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China)

  • Changhong Weng

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China)

  • Zhi Chen

    (School of Electrical Engineering and Automation, Wuhan University, Wuhan 430000, China)

Abstract

With the tight coupling of multi-energy systems, accurate multiple-load forecasting will be the primary premise for the optimal operation of integrated energy systems. Therefore, this paper proposes a Copula correlation analysis combined with deep bidirectional long and short-term memory neural network forecasting model. First, Copula correlation analysis is used to conduct correlation analysis on multiple loads and various influencing factors. The influencing factors that have a great correlation with multiple loads were screened out as the input feature set of the model to eliminate the influence of interfering factors. Then, a deep bidirectional long and short-term memory neural network was constructed. Combined with the input feature set screened by the Copula correlation analysis method, the useful information contained in the historical data was more comprehensively learned from the forward and backward directions for training and forecasting. Through the actual calculation example analysis and comparison with other models, the forecasting accuracy of the method presented in this paper was improved to a certain extent.

Suggested Citation

  • Jieyun Zheng & Linyao Zhang & Jinpeng Chen & Guilian Wu & Shiyuan Ni & Zhijian Hu & Changhong Weng & Zhi Chen, 2021. "Multiple-Load Forecasting for Integrated Energy System Based on Copula-DBiLSTM," Energies, MDPI, vol. 14(8), pages 1-14, April.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:8:p:2188-:d:535957
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    References listed on IDEAS

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    1. Samer Chaaraoui & Matthias Bebber & Stefanie Meilinger & Silvan Rummeny & Thorsten Schneiders & Windmanagda Sawadogo & Harald Kunstmann, 2021. "Day-Ahead Electric Load Forecast for a Ghanaian Health Facility Using Different Algorithms," Energies, MDPI, vol. 14(2), pages 1-22, January.
    2. Li, Yanying & Che, Jinxing & Yang, Youlong, 2018. "Subsampled support vector regression ensemble for short term electric load forecasting," Energy, Elsevier, vol. 164(C), pages 160-170.
    3. Niu, Zhewen & Yu, Zeyuan & Tang, Wenhu & Wu, Qinghua & Reformat, Marek, 2020. "Wind power forecasting using attention-based gated recurrent unit network," Energy, Elsevier, vol. 196(C).
    4. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2020. "Building thermal load prediction through shallow machine learning and deep learning," Applied Energy, Elsevier, vol. 263(C).
    5. Zheng, Zhuang & Chen, Hainan & Luo, Xiaowei, 2019. "A Kalman filter-based bottom-up approach for household short-term load forecast," Applied Energy, Elsevier, vol. 250(C), pages 882-894.
    6. Ruijin Zhu & Weilin Guo & Xuejiao Gong, 2019. "Short-Term Load Forecasting for CCHP Systems Considering the Correlation between Heating, Gas and Electrical Loads Based on Deep Learning," Energies, MDPI, vol. 12(17), pages 1-18, August.
    7. Ma, Huan & Chen, Qun & Hu, Bo & Sun, Qinhan & Li, Tie & Wang, Shunjiang, 2021. "A compact model to coordinate flexibility and efficiency for decomposed scheduling of integrated energy system," Applied Energy, Elsevier, vol. 285(C).
    8. Jin Sol Hwang & Ismi Rosyiana Fitri & Jung-Su Kim & Hwachang Song, 2020. "Optimal ESS Scheduling for Peak Shaving of Building Energy Using Accuracy-Enhanced Load Forecast," Energies, MDPI, vol. 13(21), pages 1-18, October.
    9. Happy Aprillia & Hong-Tzer Yang & Chao-Ming Huang, 2019. "Optimal Decomposition and Reconstruction of Discrete Wavelet Transformation for Short-Term Load Forecasting," Energies, MDPI, vol. 12(24), pages 1-23, December.
    10. Liu, Da & Sun, Kun, 2019. "Random forest solar power forecast based on classification optimization," Energy, Elsevier, vol. 187(C).
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    Cited by:

    1. Peng Song & Zhisheng Zhang, 2023. "Research on Multiple Load Short-Term Forecasting Model of Integrated Energy Distribution System Based on Mogrifier-Quantum Weighted MELSTM," Energies, MDPI, vol. 16(9), pages 1-13, April.
    2. Tan, Mao & Liao, Chengchen & Chen, Jie & Cao, Yijia & Wang, Rui & Su, Yongxin, 2023. "A multi-task learning method for multi-energy load forecasting based on synthesis correlation analysis and load participation factor," Applied Energy, Elsevier, vol. 343(C).
    3. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
    4. Venkataramana Veeramsetty & Arjun Mohnot & Gaurav Singal & Surender Reddy Salkuti, 2021. "Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models," Energies, MDPI, vol. 14(11), pages 1-21, May.
    5. Akash Kumar & Bing Yan & Ace Bilton, 2022. "Machine Learning-Based Load Forecasting for Nanogrid Peak Load Cost Reduction," Energies, MDPI, vol. 15(18), pages 1-23, September.

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