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dc.contributor.authorTuomiranta, Arttu
dc.contributor.authorAlet, Pierre-Jean
dc.contributor.authorBallif, Christophe
dc.contributor.authorGhedira, Hosni
dc.identifier.citationSolar Energy, Volume 237, Pages 239-252en_US
dc.description.abstractThe emergence of bifacial photovoltaics as a serious option for large-scale power generation makes the accuracy of the in-situ estimation of ground surface albedo increasingly critical. Recent studies have evaluated the performance of different models but an investigation of how these models should be calibrated is missing. This paper proposes geographically generalised parametrisations for time-variant albedo models of vegetated and desert surfaces based on a worldwide database and reports their performance compared to site-specific, unbiased parametrisations. These geographically generalised formulations can be used as an alternative for lengthy calibration campaigns as their only parameters can be easily measured. The paper also presents a simple method for deriving white-sky albedo without the need for diffuse irradiance data. The method is founded both theoretically and empirically. If on-site measurements can be collected to calibrate a model, the duration of the campaign is usually a few weeks at the maximum. This paper first shows evidence of the strong impact of the timing on calibration performance and accordingly gives practical guidelines on the most suitable measurement seasons and weather conditions. In general, early-summer months appear to be the most appropriate time for calibrating most models. Atmospheric turbidity during the campaign period, however, should be either minimised or maximised depending on the site and the model. Finally, in case no flexibility exists in timing the calibration, simple models with few parameters should be deployed. Geometric mean shows the most robust performance at most sites.en_US
dc.titleCalibration of ground surface albedo modelsen_US
dc.type.csemresearchareasDigital Energyen_US
dc.type.csemresearchareasPV & Solar Buildingsen_US

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