Nonparametric short-term probabilistic forecasting for solar radiation

• Nonparametric bootstrapping for generating prediction intervals of solar radiance.
• Mapping sun positions accounts for the heteroscedasticity of solar radiance.
• The new method delivers well calibrated and sharp prediction intervals.
• The method is straightforward and computationally efficient.

The current deep concerns on energy independence and global society’s security at the face of climate change have empowered the new “green energy” paradigm and led to a rapid development of new methodology for modeling sustainable energy resources. However, clean renewables such as wind and solar energies are inherently intermittent, and their integration into a electric power grid require accurate and reliable estimation of uncertainties. And, if probabilistic forecasting of wind power is generally well developed, probabilistic forecasting of solar power is still in its infancy. In this paper we propose a new data-driven method for constructing a full predictive density of solar radiance based on a nonparametric bootstrap. We illustrate utility of the new bootstrapped statistical ensembles for probabilistic one-hour ahead forecasting in Mildura, Australia. We show that the new approach delivers sharp and calibrated ensembles of one-hour forecasts, and is computationally inexpensive and easily tractable.