Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models

• URL: http://arxiv.org/abs/2010.04715v2
• Date: Wed, 14 Oct 2020 16:03:02 GMT
• Title: Short-Term Solar Irradiance Forecasting Using Calibrated Probabilistic Models
• Authors: Eric Zelikman, Sharon Zhou, Jeremy Irvin, Cooper Raterink, Hao Sheng, Anand Avati, Jack Kelly, Ram Rajagopal, Andrew Y. Ng, David Gagne
• Abstract summary: We train and evaluate the models using public data from seven stations in the SURFRAD network. We show that the best model, NGBoost, achieves higher performance at an intra-hourly resolution than the best benchmark solar irradiance forecasting model.
• Score: 14.579720180539136
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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