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Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts.

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Title: Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts.
Authors: Le Gal La Salle, Josselin1 (AUTHOR) josselin.le-gal-la-salle@univ-reunion.fr, Badosa, Jordi2 (AUTHOR), David, Mathieu1 (AUTHOR), Pinson, Pierre3 (AUTHOR), Lauret, Philippe1 (AUTHOR)
Source: Renewable Energy: An International Journal. Dec2020, Vol. 162, p1321-1339. 19p.
Subject Terms: Forecasting, Numerical weather forecasting, Solar system, Parametric modeling
Abstract: Accurate solar forecasts is one of the most effective solution to enhance grid operations. As the solar resource is intrinsically uncertain, a growing interest for solar probabilistic forecasts is observed in the solar research community. In this work, we compare two approaches for the generation of day-ahead solar irradiance probabilistic forecasts. The first class of models termed as deterministic-based models generates probabilistic forecasts from a deterministic value of the irradiance predicted by a Numerical Weather Prediction (NWP) model. The second type of models denoted by ensemble-based models issues probabilistic forecasts through the calibration of an Ensemble Prediction System (EPS) or from information (such as mean and variance) derived from the ensemble. The verification of the probabilistic forecasts is made using a sound framework. A numerical score, the Continuous Ranked Probability Score (CRPS), is used to assess the overall performance of the different models. The decomposition of the CRPS into reliability and resolution provides a further detailed insight into the quality of the probabilistic forecasts. In addition, a new diagnostic tool which evaluates the contribution of the statistical moments of the forecast distributions to the CRPS is proposed. This tool denoted by MC-CRPS allows identifying the characteristics of an ensemble that have an impact on the quality of the probabilistic forecasts. The assessment of the different models is done on several sites experiencing very different climatic conditions. Results show a general superior performance of ensemble-based models as the gain in forecast quality measured by the CRPS ranges from 4% to 16% depending on the site. • Probabilistic forecasts from deterministic informations and from Ensembles compared. • Several models either parametric and non-parametric are compared. • New tool to quantify the contributions of the statistical moments to the quality. • Sites with very different sky conditions are considered. • Using Ensemble Prediction Systems improve the quality of forecasts. [ABSTRACT FROM AUTHOR]
Copyright of Renewable Energy: An International Journal 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.)
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  Data: <searchLink fieldCode="JN" term="%22Renewable+Energy%3A+An+International+Journal%22">Renewable Energy: An International Journal</searchLink>. Dec2020, Vol. 162, p1321-1339. 19p.
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  Data: Accurate solar forecasts is one of the most effective solution to enhance grid operations. As the solar resource is intrinsically uncertain, a growing interest for solar probabilistic forecasts is observed in the solar research community. In this work, we compare two approaches for the generation of day-ahead solar irradiance probabilistic forecasts. The first class of models termed as deterministic-based models generates probabilistic forecasts from a deterministic value of the irradiance predicted by a Numerical Weather Prediction (NWP) model. The second type of models denoted by ensemble-based models issues probabilistic forecasts through the calibration of an Ensemble Prediction System (EPS) or from information (such as mean and variance) derived from the ensemble. The verification of the probabilistic forecasts is made using a sound framework. A numerical score, the Continuous Ranked Probability Score (CRPS), is used to assess the overall performance of the different models. The decomposition of the CRPS into reliability and resolution provides a further detailed insight into the quality of the probabilistic forecasts. In addition, a new diagnostic tool which evaluates the contribution of the statistical moments of the forecast distributions to the CRPS is proposed. This tool denoted by MC-CRPS allows identifying the characteristics of an ensemble that have an impact on the quality of the probabilistic forecasts. The assessment of the different models is done on several sites experiencing very different climatic conditions. Results show a general superior performance of ensemble-based models as the gain in forecast quality measured by the CRPS ranges from 4% to 16% depending on the site. • Probabilistic forecasts from deterministic informations and from Ensembles compared. • Several models either parametric and non-parametric are compared. • New tool to quantify the contributions of the statistical moments to the quality. • Sites with very different sky conditions are considered. • Using Ensemble Prediction Systems improve the quality of forecasts. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of Renewable Energy: An International Journal 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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        Value: 10.1016/j.renene.2020.07.042
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        Text: English
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              Text: Dec2020
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