2019/06/03 by Dazhi Yang, Elynn Wu, Jan Kleissl · 1 citation
Computer Science · Energy · Engineering · #Energy Load and Power Forecasting #Solar Radiation and Photovoltaics #Solar Thermal and Photovoltaic Systems
paper · doi:10.1016/j.ijforecast.2019.03.009
openalex publication_date 2019/06/03 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/29
Despite the significant progress made in solar forecasting over the last decade, most of the proposed models cannot be readily used by independent system operators (ISOs). This article proposes an operational solar forecasting algorithm that is closely aligned with the real-time market (RTM) forecasting requirements of the California ISO (CAISO). The algorithm first uses the North American Mesoscale (NAM) forecast system to generate hourly forecasts for a 5-h period that are issued 12 h before the actual operating hour, satisfying the lead-time requirement. Subsequently, the world’s fastest similarity search algorithm is adopted to downscale the hourly forecasts generated by NAM to a 15-min resolution, satisfying the forecast-resolution requirement. The 5-h-ahead forecasts are repeated every hour, following the actual rolling update rate of CAISO. Both deterministic and probabilistic forecasts generated using the proposed algorithm are empirically evaluated over a period of 2 years at 7 locations in 5 climate zones.