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Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System

Author

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  • Pengwei Su

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Xue Tian

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Yan Wang

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Shuai Deng

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Jun Zhao

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Qingsong An

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

  • Yongzhen Wang

    (Key Laboratory of Efficient Utilization of Low and Medium Grade Energy, Ministry of Education, Tianjin University, Tianjin 300350, China)

Abstract

The introduction of renewable resources into the distributed energy system has challenged the operation optimization of the distributed energy system. Integration of new technologies and diversified characteristics on the demand side has exerted a great influence on the distributed energy system. In this paper, by way of literature review, first, the topological structure and the mathematical expression of the distributed energy system were summarized, and the trend of enrichment and diversification and the new characteristics of the system were evaluated. Then, the load forecasting technology was reviewed and analyzed from two aspects, fundamental research and application research. Research methods of the distributed energy system under the new trend of energies were discussed, and the boundaries of the broadened distributed energy technology were explored.

Suggested Citation

  • Pengwei Su & Xue Tian & Yan Wang & Shuai Deng & Jun Zhao & Qingsong An & Yongzhen Wang, 2017. "Recent Trends in Load Forecasting Technology for the Operation Optimization of Distributed Energy System," Energies, MDPI, vol. 10(9), pages 1-13, August.
    • Handle: RePEc:gam:jeners:v:10:y:2017:i:9:p:1303-:d:110381
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    References listed on IDEAS

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    Cited by:

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    3. Ayman Esmat & Julio Usaola & Mª Ángeles Moreno, 2018. "A Decentralized Local Flexibility Market Considering the Uncertainty of Demand," Energies, MDPI, vol. 11(8), pages 1-32, August.
    4. Venkataramana Veeramsetty & Dongari Rakesh Chandra & Francesco Grimaccia & Marco Mussetta, 2022. "Short Term Electric Power Load Forecasting Using Principal Component Analysis and Recurrent Neural Networks," Forecasting, MDPI, vol. 4(1), pages 1-16, January.
    5. Sepideh Radhoush & Bradley M. Whitaker & Hashem Nehrir, 2023. "An Overview of Supervised Machine Learning Approaches for Applications in Active Distribution Networks," Energies, MDPI, vol. 16(16), pages 1-29, August.
    6. Dietrich, Bastian & Walther, Jessica & Weigold, Matthias & Abele, Eberhard, 2020. "Machine learning based very short term load forecasting of machine tools," Applied Energy, Elsevier, vol. 276(C).
    7. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.

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