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We propose a fully probabilistic prediction model for spatially aggregatedsolar photovoltaic (PV) power production at an hourly time scale with leadtimes up to several days using weather forecasts from numerical weatherprediction systems as covariates. After an appropriate logarithmictransformation of the power production, we develop a multivariate Gaussianprediction model under a Bayesian inference framework. The model incorporatesthe temporal error correlation yielding physically consistent forecasttrajectories. Several formulations of the correlation structure are proposedand investigated. Our method is one of a few approaches that issue fullpredictive distributions for PV power production. In a case study of PV powerproduction in Germany, the method gives calibrated and skillful forecasts.

Probabilistic Forecasting of Temporal Trajectories of Regional Power Production - Part 2: Photovoltaic Solar