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dc.contributor.authorAlves, Josivan Leite
dc.contributor.authorAlmeida Filho, Adiel Teixeira de
dc.contributor.authorBradaschia, Fabrício
dc.contributor.authorPalha, Rachel Perez
dc.date.accessioned2026-08-20T10:44:23Z
dc.date.available2026-08-20T10:44:23Z
dc.date.copyright2026/04/14
dc.date.issued2026
dc.identifier.otherJCEMD4.COENG-17897.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4311197
dc.description.abstractAbstractUrban expansion, driven by economic and technological development, has intensified building construction and increased energy demand. Over the last decade, this trend has become a global challenge due to the depletion of fossil fuel reserves and ...Practical ApplicationsThis study investigated how artificial intelligence models can enhance solar energy forecasting in diverse regions. By using data collected over time, the research compared the performance of three different predictive models to ...
dc.publisherAmerican Society of Civil Engineers
dc.titleSolarisBIM.AI: Smart Sustainable Building Planning with BIM-Based Solar-Production Estimation Using Machine-Learning Radiation Forecasts
dc.typeJournal Article
dc.journal.volume152
dc.journal.issue6
dc.journal.titleJournal of Construction Engineering and Management
dc.identifier.doi10.1061/JCEMD4.COENG-17897
dc.journal.fristpage04026076-1
dc.journal.lastpage04026076-17
dc.page17
dc.treeJournal of Construction Engineering and Management:;2026:;Volume ( 152 ):;issue: 006
dc.contenttypeFulltext


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