Operational solar forecasting for the real-time market

Dazhi Yang, Elynn Wu and Jan Kleissl

International Journal of Forecasting, 2019, vol. 35, issue 4, 1499-1519

Abstract: 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.

Keywords: Solar forecasting; Ensemble; Numerical weather prediction; Operational forecasting; Real-time market (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (25)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:35:y:2019:i:4:p:1499-1519

DOI: 10.1016/j.ijforecast.2019.03.009

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