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A new hourly dataset for photovoltaic energy production for the continental USA

This new dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. The simulations are carried out in three steps. First, a weather forecast system is used for the predictions of incoming insolation; then, forecast ensembles with 21 members are generated usi...

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Detalles Bibliográficos
Autores principales: Hu, Weiming, Cervone, Guido, Merzky, Andre, Turilli, Matteo, Jha, Shantenu
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813589/
https://www.ncbi.nlm.nih.gov/pubmed/35141367
http://dx.doi.org/10.1016/j.dib.2022.107824
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author Hu, Weiming
Cervone, Guido
Merzky, Andre
Turilli, Matteo
Jha, Shantenu
author_facet Hu, Weiming
Cervone, Guido
Merzky, Andre
Turilli, Matteo
Jha, Shantenu
author_sort Hu, Weiming
collection PubMed
description This new dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. The simulations are carried out in three steps. First, a weather forecast system is used for the predictions of incoming insolation; then, forecast ensembles with 21 members are generated using the Analog Ensemble technique; finally, each ensemble member is used to simulate 13 different solar panels. In total, there are [Formula: see text] simulated scenarios. Simulations are carried out for the entire year 2019, with a temporal resolution of one hour, and a spatial resolution of 12 km. The data provide a high spatio-temporal analysis of the power production under different weather and engineering scenarios. The size of the entire dataset is about 1 TB but can be openly accessed by days and scenarios. Details on how to access and use such a dataset are provided in this article.
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spelling pubmed-88135892022-02-08 A new hourly dataset for photovoltaic energy production for the continental USA Hu, Weiming Cervone, Guido Merzky, Andre Turilli, Matteo Jha, Shantenu Data Brief Data Article This new dataset is an ensemble of solar photovoltaic energy production simulations over the continental US. The simulations are carried out in three steps. First, a weather forecast system is used for the predictions of incoming insolation; then, forecast ensembles with 21 members are generated using the Analog Ensemble technique; finally, each ensemble member is used to simulate 13 different solar panels. In total, there are [Formula: see text] simulated scenarios. Simulations are carried out for the entire year 2019, with a temporal resolution of one hour, and a spatial resolution of 12 km. The data provide a high spatio-temporal analysis of the power production under different weather and engineering scenarios. The size of the entire dataset is about 1 TB but can be openly accessed by days and scenarios. Details on how to access and use such a dataset are provided in this article. Elsevier 2022-01-13 /pmc/articles/PMC8813589/ /pubmed/35141367 http://dx.doi.org/10.1016/j.dib.2022.107824 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Data Article
Hu, Weiming
Cervone, Guido
Merzky, Andre
Turilli, Matteo
Jha, Shantenu
A new hourly dataset for photovoltaic energy production for the continental USA
title A new hourly dataset for photovoltaic energy production for the continental USA
title_full A new hourly dataset for photovoltaic energy production for the continental USA
title_fullStr A new hourly dataset for photovoltaic energy production for the continental USA
title_full_unstemmed A new hourly dataset for photovoltaic energy production for the continental USA
title_short A new hourly dataset for photovoltaic energy production for the continental USA
title_sort new hourly dataset for photovoltaic energy production for the continental usa
topic Data Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8813589/
https://www.ncbi.nlm.nih.gov/pubmed/35141367
http://dx.doi.org/10.1016/j.dib.2022.107824
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