Evaluating ensemble post-processing for probabilistic energy prediction

Probabilistic forecasts based on ensemble simulations of numerical weather prediction models have become a standard tool in weather forecasting and various application areas. However, ensemble forecasting systems tend to exhibit systematic errors such as biases, and fail to correctly quantify forecast uncertainty. Therefore, a variety of post-processing methods has been developed to correct these errors and improve predictions [1]. In particular, machine learning methods based on neural networks have been demonstrated to lead to substantial improvements compared to classical statistical techniques [2].While post-processing can successfully correct the biases and dispersion errors in the weather variables, its effect but has not been evaluated thoroughly in the context of subsequent forecasts, such as wind and solar power generation forecasts and it is not obvious how to best propagate forecast uncertainty through to subsequent power forecasting models. Therefore, the work presented here will evaluate multiple strategies for applying ensemble post-processing to probabilistic wind and solar power forecasts. We use Ensemble Model Output Statistics (EMOS) as the post-processing method and evaluate four possible strategies: only using the raw ensembles without post-processing, a one-step strategy where only the weather ensembles are post-processed, a one-step strategy where we only post-process the power ensembles and a two-step strategy where we post-process both the weather and power ensembles. The presentation is based on recent work in Phipps et al. (2022) [3] and ongoing other work.References[1] Vannitsem, S., et al. (2021). Statistical Postprocessing for Weather Forecasts - Review, Challenges and Avenues in a Big Data World. Bulletin of the American Meteorological Society, 102, E681–E699.[2] Rasp, S. and Lerch, S. (2018). Neural networks for post-processing ensemble weather forecasts. Monthly Weather Review, 146, 3885–3900.[3] Phipps, K., Lerch, S., Andersson, M., Mikut, R., Hagenmeyer, V. and Ludwig, N. (2022). Evaluating ensemble post-processing for wind power forecasts. Wind Energy, 25, 1379-1405.