| Abstract: |
| Photovoltaic (PV) systems are widely spread across MV and LV distribution systems and the penetration of PV
generation is solidly growing. Because of the uncertain nature of the solar energy resource, PV power forecasting
models are crucial in any energy management system for smart distribution networks. Although point forecasts can
suit many scopes, probabilistic forecasts add further flexibility to an energy management system and are
recommended to enable a wider range of decision making and optimization strategies. This paper proposes
methodology towards probabilistic PV power forecasting based on a Bayesian bootstrap quantile regression model,
in which a Bayesian bootstrap is applied to estimate the parameters of a quantile regression model. A novel
procedure is presented to optimize the extraction of the predictive quantiles from the bootstrapped estimation of
the related coefficients, raising the predictive ability of the final forecasts. Numerical experiments based on actual
data quantify an enhancement of the performance of up to 2.2% when compared to relevant benchmarks. |
| Key words: Bayesian bootstrap, Photovoltaic systems, Probabilistic forecasting, Renewable generation smart grids |
| DOI：10.1186/s41601-020-00167-7 |
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| Fund: |
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