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Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting

Author

Listed:
  • Tian Shi

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Fei Mei

    (College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China)

  • Jixiang Lu

    (State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China)

  • Jinjun Lu

    (State Key Laboratory of Smart Grid Protection and Control, NARI Group Corporation, Nanjing 211000, China)

  • Yi Pan

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Cheng Zhou

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jianzhang Wu

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

  • Jianyong Zheng

    (School of Electrical Engineering, Southeast University, Nanjing 210096, China)

Abstract

With the refinement and intelligence of power system optimal dispatching, the widespread adoption of advanced grid applications that consider the safety and economy of power systems, and the massive access of distributed energy resources, the requirement for bus load prediction accuracy is continuously increasing. Aiming at the volatility brought about by the large-scale access of new energy sources, the adaptability to different forecasting horizons and the time series characteristics of the load, this paper proposes a phase space reconstruction (PSR) and deep belief network (DBN)-based very short-term bus load prediction model. Cross-validation is also employed to optimize the structure of the DBN. The proposed PSR-DBN very short-term bus load forecasting model is verified by applying the real measured load data of a substation. The results prove that, when compared to other alternative models, the PSR-DBN model has higher prediction accuracy and better adaptability for different forecasting horizons in the case of high distributed power penetration and large fluctuation of bus load.

Suggested Citation

  • Tian Shi & Fei Mei & Jixiang Lu & Jinjun Lu & Yi Pan & Cheng Zhou & Jianzhang Wu & Jianyong Zheng, 2019. "Phase Space Reconstruction Algorithm and Deep Learning-Based Very Short-Term Bus Load Forecasting," Energies, MDPI, vol. 12(22), pages 1-17, November.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:22:p:4349-:d:287190
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    References listed on IDEAS

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    Cited by:

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    2. Antonio Gabaldón & María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez, 2022. "Guest Editorial: Special Issue on Short-Term Load Forecasting 2019, Results and Future Perspectives," Energies, MDPI, vol. 15(24), pages 1-5, December.
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    4. Artur Łukaszewski & Łukasz Nogal & Sylwester Robak, 2020. "Weight Calculation Alternative Methods in Prime’s Algorithm Dedicated for Power System Restoration Strategies," Energies, MDPI, vol. 13(22), pages 1-20, November.
    5. Xu Ran & Chang Xu & Lei Ma & Feifei Xue, 2022. "Wind Power Interval Prediction with Adaptive Rolling Error Correction Based on PSR-BLS-QR," Energies, MDPI, vol. 15(11), pages 1-22, June.
    6. Gabriel Trierweiler Ribeiro & João Guilherme Sauer & Naylene Fraccanabbia & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Bayesian Optimized Echo State Network Applied to Short-Term Load Forecasting," Energies, MDPI, vol. 13(9), pages 1-19, May.

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