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Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach

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  • Wang, Yi
  • Von Krannichfeldt, Leandro
  • Zufferey, Thierry
  • Toubeau, Jean-François

Abstract

The integration of distributed energy resources (DER) complicates the operation of the power distribution grids, and the nodal voltage may violate frequently. Making accurate predictions of the nodal voltage is fundamental for voltage regulation of the distribution grid. Even though energy forecasting has been widely studied, voltage is still a rarely touched area. This paper enriches the research by proposing an ensemble approach for both deterministic and probabilistic short-term nodal voltage forecasting. Specifically, a new joint model- and data-driven feature selection is first performed to select the most relevant features for distribution grid voltage forecasting. Then, different individual forecasting models are trained using the selected features. On this basis, simple weighted averaging and quantile regression averaging approaches are applied to combine the individual models for deterministic and probabilistic forecasting, respectively. Finally, case studies are conducted on a real-world distribution grid to verify the effectiveness and superiority of the proposed method.

Suggested Citation

  • Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
    • Handle: RePEc:eee:appene:v:304:y:2021:i:c:s0306261921011971
    DOI: 10.1016/j.apenergy.2021.117880
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    Cited by:

    1. Bartosz Uniejewski, 2023. "Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices," Papers 2302.00411, arXiv.org, revised Nov 2024.
    2. Bakhshideh Zad, Bashir & Toubeau, Jean-François & Bruninx, Kenneth & Vatandoust, Behzad & De Grève, Zacharie & Vallée, François, 2022. "Supervised learning-assisted modeling of flow-based domains in European resource adequacy assessments," Applied Energy, Elsevier, vol. 325(C).
    3. Yang, Mao & Che, Runqi & Yu, Xinnan & Su, Xin, 2024. "Dual NWP wind speed correction based on trend fusion and fluctuation clustering and its application in short-term wind power prediction," Energy, Elsevier, vol. 302(C).
    4. Ye, Lin & Li, Yilin & Pei, Ming & Zhao, Yongning & Li, Zhuo & Lu, Peng, 2022. "A novel integrated method for short-term wind power forecasting based on fluctuation clustering and history matching," Applied Energy, Elsevier, vol. 327(C).

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