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dc.contributor.authorTapia García, Juan Miguel 
dc.contributor.authorBaca Ruiz, Luis Gonzaga 
dc.contributor.authorCriado Ramón, David
dc.contributor.authorPegalajar Jiménez, María Del Carmen 
dc.date.accessioned2024-09-18T10:34:18Z
dc.date.available2024-09-18T10:34:18Z
dc.date.issued2024-07-03
dc.identifier.citationTapia-García, J.; Ruiz, L.G.B.; Criado-Ramón, D.; Pegalajar, M.C. Big Data Techniques Applied to Forecast Photovoltaic Energy Demand in Spain. Eng. Proc. 2024, 68, 11. https://doi.org/10.3390/engproc2024068011es_ES
dc.identifier.urihttps://hdl.handle.net/10481/94652
dc.description.abstractRenewable energies play an important role in our society’s development, addressing the challenges presented by climate change. Specifically, in countries like Spain, technologies such as solar energy assume a crucial significance, enabling the generation of clean energy. This study addresses the critical need to accurately predict photovoltaic (PV) energy demand in Spain. By using the data collected from the Spanish Electricity System, four models (Linear Regression, Random Forest, Recurrent Neural Network, and LightGBM) were implemented, with adaptations for Big Data. The LR model proved unsuitable, while the LGBM emerged as the most accurate and timely performer. The incorporation of Big Data adaptations amplifies the significance of our findings, highlighting the effectiveness of the LGBM in forecasting PV energy demand with both accuracy and efficiency.es_ES
dc.description.sponsorshipMinisterio de Ciencia e Innovación (Spain) (Grant PID2020-112495RB-C21 funded by MCIN/AEI/10.13039/501100011033)es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectPhotovoltaices_ES
dc.subjectEnergy demandes_ES
dc.subjectRenewable energyes_ES
dc.titleBig Data Techniques Applied to Forecast Photovoltaic Energy Demand in Spaines_ES
dc.typejournal articlees_ES
dc.rights.accessRightsopen accesses_ES
dc.identifier.doi10.3390/engproc2024068011
dc.type.hasVersionVoRes_ES


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