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Data driven tools to assess the location of photovoltaic facilities in urban areas
dc.contributor.author | Rodríguez-Gómez, Francisco | |
dc.contributor.author | Del-Campo-Ávila, José | |
dc.contributor.author | Ferrer Cuesta, Marta | |
dc.contributor.author | Mora-López, Llanos | |
dc.date.accessioned | 2022-06-27T10:44:07Z | |
dc.date.available | 2022-06-27T10:44:07Z | |
dc.date.issued | 2022-10-01 | |
dc.identifier.citation | Francisco Rodríguez-Gómez, José del Campo-Ávila, Marta Ferrer-Cuesta, Llanos Mora-López, Data driven tools to assess the location of photovoltaic facilities in urban areas, Expert Systems with Applications, Volume 203, 2022, 117349, ISSN 0957-4174, https://doi.org/10.1016/j.eswa.2022.117349. | es_ES |
dc.identifier.uri | https://hdl.handle.net/10630/24498 | |
dc.description.abstract | Urban sustainability is a significant factor in combating climate change. Replacing polluting by renewable energies is fundamental to reduce the emission of greenhouse gases. Photovoltaic (PV) facilities harnessing solar energy, and particularly self-consumption PV facilities, can be widely used in cities throughout most countries. Therefore, locating spaces where photovoltaic installations can be integrated into urban areas is essential to reduce climate change and improve urban sustainability. An open-source software (URSUS-PV) to aid decision-making regarding possible optimal locations for photovoltaic panel installations in cities is presented in this paper. URSUS-PV is the result of a data mining process, and it can extract the characteristics of the roofs (orientation, inclination, latitude, longitude, area) in the urban areas of interest. By combining this information with meteorological data and characteristics of the photovoltaic systems, the system can predict both the next-day hourly photovoltaic energy production and the long-term photovoltaic daily average energy production. | es_ES |
dc.description.sponsorship | This work has been supported by the project RTI2018-095097-B-I00 at the 2018 call for I+D+i Project of the Ministerio de Ciencia, Innovación y Universidades, Spain. Funding for open access charge: Universidad de Málaga/CBUA, Spain . | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Elsevier | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/ | * |
dc.subject | Recursos energéticos renovables | es_ES |
dc.subject.other | Renewable energy | es_ES |
dc.subject.other | Photovoltaic systems | es_ES |
dc.subject.other | LiDAR images Semantic segmentation | es_ES |
dc.subject.other | Roof feature extraction | es_ES |
dc.subject.other | Energy forecas | es_ES |
dc.title | Data driven tools to assess the location of photovoltaic facilities in urban areas | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.centro | E.T.S.I. Informática | es_ES |
dc.identifier.doi | https://doi.org/10.1016/j.eswa.2022.117349 | |
dc.rights.cc | Atribución 4.0 Internacional | * |
dc.type.hasVersion | info:eu-repo/semantics/publishedVersion | es_ES |