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Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models

Author

Listed:
  • Tawsif Ahmad

    (Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, USA)

  • Ning Zhou

    (Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, USA)

  • Ziang Zhang

    (Department of Electrical and Computer Engineering, State University of New York at Binghamton, Binghamton, NY 13902, USA)

  • Wenyuan Tang

    (Department of Electrical and Computer Engineering, North Carolina State University, Raleigh, NC 27695, USA)

Abstract

Accurate quantification of uncertainty in solar photovoltaic (PV) generation forecasts is imperative for the efficient and reliable operation of the power grid. In this paper, a data-driven non-parametric probabilistic method based on the Naïve Bayes (NB) classification algorithm and Dempster–Shafer theory (DST) of evidence is proposed for day-ahead probabilistic PV power forecasting. This NB-DST method extends traditional deterministic solar PV forecasting methods by quantifying the uncertainty of their forecasts by estimating the cumulative distribution functions (CDFs) of their forecast errors and forecast variables. The statistical performance of this method is compared with the analog ensemble method and the persistence ensemble method under three different weather conditions using real-world data. The study results reveal that the proposed NB-DST method coupled with an artificial neural network model outperforms the other methods in that its estimated CDFs have lower spread, higher reliability, and sharper probabilistic forecasts with better accuracy.

Suggested Citation

  • Tawsif Ahmad & Ning Zhou & Ziang Zhang & Wenyuan Tang, 2024. "Enhancing Probabilistic Solar PV Forecasting: Integrating the NB-DST Method with Deterministic Models," Energies, MDPI, vol. 17(10), pages 1-19, May.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:10:p:2392-:d:1395942
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    References listed on IDEAS

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    1. Kaur, Amanpreet & Nonnenmacher, Lukas & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2016. "Benefits of solar forecasting for energy imbalance markets," Renewable Energy, Elsevier, vol. 86(C), pages 819-830.
    2. Juan M. Morales & Antonio J. Conejo & Henrik Madsen & Pierre Pinson & Marco Zugno, 2014. "Integrating Renewables in Electricity Markets," International Series in Operations Research and Management Science, Springer, edition 127, number 978-1-4614-9411-9, April.
    3. Alessandrini, S. & Delle Monache, L. & Sperati, S. & Cervone, G., 2015. "An analog ensemble for short-term probabilistic solar power forecast," Applied Energy, Elsevier, vol. 157(C), pages 95-110.
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