2020/10/09 by Eric Zelikman, Sharon Zhou, Zelikman, Eric +16
Computer Science · Energy · Engineering · #Applications (stat.AP) #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Photovoltaic System Optimization Techniques #Solar Radiation and Photovoltaics
paper · pdf · doi:10.48550/arxiv.2010.04715
openalex publication_date 2020/10/09 · openalex created_date 2020/10/15 · openalex updated_date 2026/07/28
Advancing probabilistic solar forecasting methods is essential to supporting the integration of solar energy into the electricity grid. In this work, we develop a variety of state-of-the-art probabilistic models for forecasting solar irradiance. We investigate the use of post-hoc calibration techniques for ensuring well-calibrated probabilistic predictions. We train and evaluate the models using public data from seven stations in the SURFRAD network, and demonstrate that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model across all stations. Further, we show that NGBoost with CRUDE post-hoc calibration achieves comparable performance to a numerical weather prediction model on hourly-resolution forecasting.