A simple method for quickly estimating solar irradiance forecast errors
Year: 2025
Type: article
Abstract: In this work, we develop simple linear models that allow users to predict solar irradiance forecast errors based solely on solar variability at a specific location on Earth. These straightforward yet actionable models enable solar forecasters to quickly estimate forecast errors for a given site, providing a clear indication of how well their forecasting models are likely to perform. The error in deterministic solar irradiance forecasts is measured by the Root Mean Square Error (RMSE), while solar variability is quantified by the standard deviation of an hourly time series of changes in the dimensionless clear sky index. Sixty sites distributed around the globe are used to build two types of RMSE prediction models. The first type is for intra-day forecasts (1-hour to 6-hour forecast horizons), while the second is for day-ahead forecasts (24-hour horizon). The derivation of the intra-day forecast error prediction model leverages on a non-linear time series approach whereas the one for day-ahead forecast error relies on forecasts issued by the European Centre for Medium-Range Weather Forecasts (ECMWF). For each type of model, we calculate also the 2.5% and 97.5% percentiles of the distribution in order to estimate the 95% uncertainty interval associated with the prediction. This uncertainty interval defines the bounds of the RMSE within which 95% of future RMSE values are expected to fall. These error bounds can provide solar forecasters with valuable insights into the performance of their solar forecasting methods in relation to the forecast challenges posed by site-specific variability. Verification against published results in the literature, specifically for seven sites of the SURFRAD network, demonstrates that these models can satisfactorily predict intra-day and day-ahead forecast RMSEs using only site-specific solar variability data.
Source: Solar Energy
Institutions: University of Reunion Island, Fraunhofer Institute for Solar Energy Systems, State University of New York, University at Albany, State University of New York, Centre National de la Recherche Scientifique, Université de Corse Pascal Paoli, Centre Observation, Impacts, Énergie, École Nationale Supérieure des Mines de Paris
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
Estimated APC paid: $3,900
Funders: Sveriges Meteorologiska och Hydrologiska Institut, European Neuroendocrine Association, University of Oregon, Cairo University, Chulalongkorn University, Petrobras, Centre National de la Recherche Scientifique, Danmarks Tekniske Universitet, Chiba University, Karl-Franzens-Universität Graz, Bureau of Meteorology, Australian Government, National Oceanic and Atmospheric Administration, HORIZON EUROPE Framework Programme, Institut de Recherche en Energie Solaire et Energies Nouvelles
OpenAlex ID: W4413478353