Probabilistic Forecasting of Temporal Trajectories of Regional Power Production - Part 1: Wind

Abstract: Renewable energy sources provide a constantly increasing contribution to the total energy production worldwide. However, the power generation from these sources is highly variable due to their dependence on meteorological conditions. Accurate forecasts for the production at various temporal and spatial scales are thus needed for an efficiently operating electricity market. In this article - part 1 - we propose fully probabilistic prediction models for spatially aggregated wind power production at an hourly time scale with lead times up to several days using weather forecasts from numerical weather prediction systems as covariates. After an appropriate cubic transformation of the power production, we build up a multivariate Gaussian prediction model under a Bayesian inference framework which incorporates the temporal error correlation. In an application to predict wind production in Germany, the method provides calibrated and skillful forecasts. Comparison is made between several formulations of the correlation structure.