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

Tawsif Ahmad, Ning Zhou (), Ziang Zhang and Wenyuan Tang
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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

Energies, 2024, vol. 17, issue 10, 1-19

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.

Keywords: continuous rank probability score; Dempster–Shafer theory; naïve Bayes classification; probabilistic solar power forecasting; uncertainty quantification (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2024
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