Statistical learning for NWP post-processing: A benchmark for solar irradiance forecasting. (15th May 2022)

• Record Type: Journal Article
• Authors: Verbois, Hadrien; Saint-Drenan, Yves-Marie; Thiery, Alexandre; Blanc, Philippe
• Is Part Of: Solar energy. Volume 238(2022)
• Page Start: 132
• Page End: 149
• Publication Date: 2022-05-15
• Subjects: Solar irradiance forecasting -- Machine Learning -- NWP post-processing -- Benchmark; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2022.03.017
• Languages: English
• ISSNs: 0038-092X

Abstract

Abstract: The share of solar power in the global and local energy mixes has increased dramatically in the past decade. Consequently, there has been a significant rise in the interest for solar power forecasting, for different time horizons, ranging from few minutes to seasons. For day-ahead forecasts, combination of Numerical Weather Prediction (NWP) models and post-processing algorithms is the most popular approach. Many recent publications have proposed innovative NWP post-processing methods. However, because different works use different datasets, metrics, and even cross-validation methods, it is rarely possible to fairly compare results across several papers. In this work, we propose a rigorous benchmark of several solar NWP post-processing models representative of the literature. For our results to be as general as possible, the comparison is conducted with an open dataset, over 6 years and 7 locations. In addition, we propose a novel benchmarking approach, that focuses on the systematicity of the ranking of forecasting models. Our results show that, when used in combination with proper regularization, large predictor sets are systematically beneficial to NWP post-processing methods. They also demonstrate that more complex algorithms such as neural networks and gradient boosting generally have the lowest mean square error, while support vector regression, a more parsimonious algorithm, performs better in terms of mean absolute error. Lastly, the focus given to ranking systematicity reveals that no model is better in all occasions. This means that researchers should be measured when they conclude to the superiority of a model, in particular when testing data is scarce. Highlights: 16 NWP post-processing algorithms are tested over 6 years and 7 stations. A novel comparison approach focusing on the analysis of systematicity is proposed. The choice of predictor set is more impactful than that of the regression algorithm. Proper regularization is necessary to benefit from a larger predictor set. Studying the systematicity of two models' relative performances is critical.