Probabilistic forecasting of day-ahead solar irradiance using quantile gradient boosting. (October 2018)

• Record Type: Journal Article
• Authors: Verbois, Hadrien; Rusydi, Andrivo; Thiery, Alexandre
• Is Part Of: Solar energy. Volume 173(2018)
• Page Start: 313
• Page End: 327
• Publication Date: 2018-10
• Subjects: Probabilistic prediction -- Solar irradiance forecasting -- Statistical learning -- NWP post-processing; Solar energy -- Periodicals; Solar engines -- Periodicals; 621.47
• DOI: 10.1016/j.solener.2018.07.071
• Languages: English
• ISSNs: 0038-092X

Abstract

Highlights: A novel method for probabilistic forecasting of solar irradiance is proposed. The proposed method is competitive with Lasso and analog ensembles. The benefit of using a large set of WRF outputs for post-processing is demonstrated. Abstract: Due to the chaotic nature of the underlying physical processes, even state-of-the-art models cannot perfectly forecast the solar irradiance at the surface of the earth. There is, therefore, a growing interest in the research community for forecasting methods that can quantify their own uncertainty. This paper proposes a novel probabilistic framework for forecasting day-ahead hourly solar irradiance. A principal component analysis (PCA) is used to tightly combine a high-resolution mesoscale numerical weather prediction (NWP) model with a quantile gradient boosting algorithm. A thorough evaluation of the deterministic and probabilistic properties of the model is conducted for a full year in the tropical island of Singapore. The impact of the sky conditions on its performance is also considered. Furthermore, a rigorous statistical framework is employed to systematically benchmark our model against two state of the art methods, a Lasso model output statistic procedure and an analog ensemble (AnEn). Our model significantly improves the numerical weather prediction model: it achieves a 41% reduction of the MAE and 39% reduction of the RMSE. It is also slightly more accurate than Lasso and has a CRPS 4% lower than that of AnEn.