A simple method for quickly estimating solar irradiance forecast errors.
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| Title: | A simple method for quickly estimating solar irradiance forecast errors. |
|---|---|
| Authors: | Lauret, Philippe1 (AUTHOR) philippe.lauret@univ-reunion.fr, David, Mathieu1 (AUTHOR) mathieu.david@univ-reunion.fr, Salle, Josselin Le Gal La1 (AUTHOR) josselin.le-gal-la-salle@univ-reunion.fr, Lorenz, Elke2 (AUTHOR) elke.lorenz@ise.fraunhofer.de, Perez, Richard3 (AUTHOR) solarperez@gmail.com, Voyant, Cyril4,5 (AUTHOR) cyril.voyant@minesparis.psl.eu |
| Source: | Solar Energy. Nov2025, Vol. 301, pN.PAG-N.PAG. 1p. |
| Subjects: | Solar radiation, Forecasting, Confidence intervals, Linear statistical models, Standard deviations, Sun |
| 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. • We build linear models to predict solar forecast errors from solar variability. • Intra-day and day-ahead predictions models are proposed. • Models estimate 95% uncertainty bounds for future forecast verification. • Verification shows good RMSE predictions for diverse global and SURFRAD sites. [ABSTRACT FROM AUTHOR] |
| Copyright of Solar Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
| FullText | Text: Availability: 0 |
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| Header | DbId: egs DbLabel: Engineering Source An: 188679244 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: A simple method for quickly estimating solar irradiance forecast errors. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Lauret%2C+Philippe%22">Lauret, Philippe</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> philippe.lauret@univ-reunion.fr</i><br /><searchLink fieldCode="AR" term="%22David%2C+Mathieu%22">David, Mathieu</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> mathieu.david@univ-reunion.fr</i><br /><searchLink fieldCode="AR" term="%22Salle%2C+Josselin+Le+Gal+La%22">Salle, Josselin Le Gal La</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> josselin.le-gal-la-salle@univ-reunion.fr</i><br /><searchLink fieldCode="AR" term="%22Lorenz%2C+Elke%22">Lorenz, Elke</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> elke.lorenz@ise.fraunhofer.de</i><br /><searchLink fieldCode="AR" term="%22Perez%2C+Richard%22">Perez, Richard</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> solarperez@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Voyant%2C+Cyril%22">Voyant, Cyril</searchLink><relatesTo>4,5</relatesTo> (AUTHOR)<i> cyril.voyant@minesparis.psl.eu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Solar+Energy%22">Solar Energy</searchLink>. Nov2025, Vol. 301, pN.PAG-N.PAG. 1p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Solar+radiation%22">Solar radiation</searchLink><br /><searchLink fieldCode="DE" term="%22Forecasting%22">Forecasting</searchLink><br /><searchLink fieldCode="DE" term="%22Confidence+intervals%22">Confidence intervals</searchLink><br /><searchLink fieldCode="DE" term="%22Linear+statistical+models%22">Linear statistical models</searchLink><br /><searchLink fieldCode="DE" term="%22Standard+deviations%22">Standard deviations</searchLink><br /><searchLink fieldCode="DE" term="%22Sun%22">Sun</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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. • We build linear models to predict solar forecast errors from solar variability. • Intra-day and day-ahead predictions models are proposed. • Models estimate 95% uncertainty bounds for future forecast verification. • Verification shows good RMSE predictions for diverse global and SURFRAD sites. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Solar Energy is the property of Pergamon Press - An Imprint of Elsevier Science and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1016/j.solener.2025.113821 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 1 StartPage: N.PAG Subjects: – SubjectFull: Solar radiation Type: general – SubjectFull: Forecasting Type: general – SubjectFull: Confidence intervals Type: general – SubjectFull: Linear statistical models Type: general – SubjectFull: Standard deviations Type: general – SubjectFull: Sun Type: general Titles: – TitleFull: A simple method for quickly estimating solar irradiance forecast errors. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Lauret, Philippe – PersonEntity: Name: NameFull: David, Mathieu – PersonEntity: Name: NameFull: Salle, Josselin Le Gal La – PersonEntity: Name: NameFull: Lorenz, Elke – PersonEntity: Name: NameFull: Perez, Richard – PersonEntity: Name: NameFull: Voyant, Cyril IsPartOfRelationships: – BibEntity: Dates: – D: 15 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 0038092X Numbering: – Type: volume Value: 301 Titles: – TitleFull: Solar Energy Type: main |
| ResultId | 1 |
