{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["11"],"submitter":["Isik M"],"pubmed_abstract":["Accurate prediction of solar irradiance is critical for optimizing solar energy systems, enhancing grid stability, and supporting sustainable energy transitions. While numerous studies have explored various methodologies for solar radiation prediction, challenges remain in achieving high accuracy across diverse geographic locations and temporal resolutions. This study presents a novel hybrid model combining temporal convolutional networks (TCN), Transformer encoders (TE), and artificial neural networks (ANN) to predict global horizontal irradiance (GHI) with high precision. Utilizing a comprehensive dataset from three significant U.S. solar energy sites-Desert Sunlight, Copper Mountain, and Solar Star-spanning 22 years at a 30-min temporal resolution, the proposed model demonstrated superi"],"journal":["PeerJ. Computer science"],"pagination":["e3026"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12453754"],"repository":["biostudies-literature"],"pubmed_title":["A novel hybrid TCN-TE-ANN model for high-precision solar irradiance prediction."],"pmcid":["PMC12453754"],"pubmed_authors":["Isik M"],"additional_accession":[]},"is_claimable":false,"name":"A novel hybrid TCN-TE-ANN model for high-precision solar irradiance prediction.","description":"Accurate prediction of solar irradiance is critical for optimizing solar energy systems, enhancing grid stability, and supporting sustainable energy transitions. While numerous studies have explored various methodologies for solar radiation prediction, challenges remain in achieving high accuracy across diverse geographic locations and temporal resolutions. This study presents a novel hybrid model combining temporal convolutional networks (TCN), Transformer encoders (TE), and artificial neural networks (ANN) to predict global horizontal irradiance (GHI) with high precision. Utilizing a comprehensive dataset from three significant U.S. solar energy sites-Desert Sunlight, Copper Mountain, and Solar Star-spanning 22 years at a 30-min temporal resolution, the proposed model demonstrated superi","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025","modification":"2026-04-30T03:14:15.988Z","creation":"2026-04-30T03:10:57.353Z"},"accession":"S-EPMC12453754","cross_references":{"pubmed":["40989396"],"doi":["10.7717/peerj-cs.3026"]}}