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

Journal Article · · Energies
DOI:https://doi.org/10.3390/en17102392· OSTI ID:2350983
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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.

Research Organization:
Binghamton University, NY (United States)
Sponsoring Organization:
U.S. National Science Foundation; USDOE Office of Energy Efficiency and Renewable Energy (EERE)
Grant/Contract Number:
EE0009341
OSTI ID:
2350983
Alternate ID(s):
OSTI ID: 2369570
Journal Information:
Energies, Journal Name: Energies Journal Issue: 10 Vol. 17; ISSN 1996-1073; ISSN ENERGA
Publisher:
MDPI AGCopyright Statement
Country of Publication:
Switzerland
Language:
English

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Related Subjects

14 SOLAR ENERGY
Dempster–Shafer theory
continuous rank probability score
naïve Bayes classification
probabilistic solar power forecasting
uncertainty quantification