{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(1)"],"submitter":["Ahmed U"],"pubmed_abstract":["The transition to sustainable energy has become imperative due to the depletion of fossil fuels. Solar energy presents a viable alternative owing to its abundance and environmental benefits. However, the intermittent nature of solar energy requires accurate forecasting of solar irradiance (SI) for reliable operation of photovoltaics (PVs) integrated systems. Traditional deep learning (DL) models and decision tree (DT)-based algorithms have been widely employed for this purpose. However, DL models often demand substantial computational resources and large datasets, while DT algorithms lack generalizability. To address these limitations, this study proposes a novel parallel boosting neural network (PBNN) framework that integrates boosting algorithms with a feedforward neural network (FFNN). "],"journal":["Scientific reports"],"pagination":["11642"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC11971244"],"repository":["biostudies-literature"],"pubmed_title":["Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting."],"pmcid":["PMC11971244"],"pubmed_authors":["Aziz I","Mahmood A","Khan AR","Alimgeer KS","Razzaq S","Hammad A","Ahmed U","Kuhlmann L"],"additional_accession":[]},"is_claimable":false,"name":"Parallel boosting neural network with mutual information for day-ahead solar irradiance forecasting.","description":"The transition to sustainable energy has become imperative due to the depletion of fossil fuels. Solar energy presents a viable alternative owing to its abundance and environmental benefits. However, the intermittent nature of solar energy requires accurate forecasting of solar irradiance (SI) for reliable operation of photovoltaics (PVs) integrated systems. Traditional deep learning (DL) models and decision tree (DT)-based algorithms have been widely employed for this purpose. However, DL models often demand substantial computational resources and large datasets, while DT algorithms lack generalizability. To address these limitations, this study proposes a novel parallel boosting neural network (PBNN) framework that integrates boosting algorithms with a feedforward neural network (FFNN). ","dates":{"release":"2025-01-01T00:00:00Z","publication":"2025 Apr","modification":"2025-07-13T03:04:38.537Z","creation":"2025-07-13T03:04:38.537Z"},"accession":"S-EPMC11971244","cross_references":{"pubmed":["40185800"],"doi":["10.1038/s41598-025-95891-1"]}}