Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Logeshwaran, J.; Yadav, Satya Prakash; Altrjman, Chadi; Al-Turjman, Fadi Title A name given to the resource An Improved Deep Learning Framework for Energy Management in Low-Energy Building Integrated Photovoltaics (LE-BIPV) Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Green Energy and Technology;pp.427-436 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-3-031-98997-1_45" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-3-031-98997-1_45</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039102369?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039102369?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Logeshwaran J., Department of Computer Science, Christ University, Karnataka, Bengaluru, India; Yadav S.P., School of Computer Science Engineering and Technology (SCSET), Bennett University, U.P, Greater Noida, India; Altrjman C., Department of Chemical Engineering, Waterloo University, Waterloo, ON, Canada; Al-Turjman F., Artificial Intelligence, Software, and Information Systems Engineering Departments, Research Center for AI and IoT, AI and Robotics Institute, Near East University, Mersin, Turkey Description An account of the resource The possibility of incorporating photovoltaics (PV) as part of building design has opened a new approach to energy generation from sustainable resources. An effective method to facilitate the good operation of these systems would be efficient energy-level management. The existing Energy Management of LE-BIPV employs a conventional control strategy, which is inconvenient for operation and fails to properly deal with nonlinearity in the PV system. The proposed model aims to provide a new deep-learning framework for the energy management of LE-BIPV. The proposed neural network framework can learn the intricate relationships between PV generation and battery storage and enable accurate energy management predictions. This proposed deep learning framework can substantially upgrade the global energy control of building-integrated PV systems in low-energy buildings. The Author(s), under exclusive license to Springer Nature Switzerland AG 2026. Subject The topic of the resource Building; Deep Learning; Energy; Neural Networks; Photovoltaics; Solar Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18653529; ISBN: 978-303198996-4; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online