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Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings

The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and buil...

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Main Authors:	Nur-E-Alam M., Zehad Mostofa K., Kar Yap B., Khairul Basher M., Aminul Islam M., Vasiliev M., Soudagar M.E.M., Das N., Sieh Kiong T.
Other Authors:	57197752581
Format:	Article
Published:	Elsevier Ltd 2025
Subjects:	Automation Carbon Energy efficiency Intelligent buildings Machine learning Meteorology Renewable energy Solar panels Solar power generation Sustainable development Blended systems Building applications Hybrid energy system Low-carbon emissions Machine-learning Net-zero building application Photovoltaics Sustainable building Sustainable energy Toronto building carbon emission energy efficiency machine learning photovoltaic system sustainability Hybrid systems
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id	my.uniten.dspace-36833
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spelling	my.uniten.dspace-368332025-03-03T15:45:01Z Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings Nur-E-Alam M. Zehad Mostofa K. Kar Yap B. Khairul Basher M. Aminul Islam M. Vasiliev M. Soudagar M.E.M. Das N. Sieh Kiong T. 57197752581 58880900300 58881467500 58880754700 57828419400 16053621100 57194384501 7201994841 15128307800 Automation Carbon Energy efficiency Intelligent buildings Machine learning Meteorology Renewable energy Solar panels Solar power generation Sustainable development Blended systems Building applications Hybrid energy system Low-carbon emissions Machine-learning Net-zero building application Photovoltaics Sustainable building Sustainable energy Toronto building carbon emission energy efficiency machine learning photovoltaic system sustainability Hybrid systems The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up to 78%, location dependent) and can be scaled up for entire buildings. The simulated results indicate that Middle Eastern cities are particularly suitable for these hybrid systems, generating approximately 1.2 times more power compared to Toronto, Canada. Additionally, we predict the outcome of the possible incorporation of intelligent and automated systems to boost overall energy efficiency toward achieving a sustainable building environment. ? 2024 The Author(s) Final 2025-03-03T07:45:01Z 2025-03-03T07:45:01Z 2024 Article 10.1016/j.seta.2024.103636 2-s2.0-85184773743 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85184773743&doi=10.1016%2fj.seta.2024.103636&partnerID=40&md5=7a409d73f817e1ab975890e79ad5aac9 https://irepository.uniten.edu.my/handle/123456789/36833 62 103636 Elsevier Ltd Scopus
institution	Universiti Tenaga Nasional
building	UNITEN Library
collection	Institutional Repository
continent	Asia
country	Malaysia
content_provider	Universiti Tenaga Nasional
content_source	UNITEN Institutional Repository
url_provider	http://dspace.uniten.edu.my/
topic	Automation Carbon Energy efficiency Intelligent buildings Machine learning Meteorology Renewable energy Solar panels Solar power generation Sustainable development Blended systems Building applications Hybrid energy system Low-carbon emissions Machine-learning Net-zero building application Photovoltaics Sustainable building Sustainable energy Toronto building carbon emission energy efficiency machine learning photovoltaic system sustainability Hybrid systems
spellingShingle	Automation Carbon Energy efficiency Intelligent buildings Machine learning Meteorology Renewable energy Solar panels Solar power generation Sustainable development Blended systems Building applications Hybrid energy system Low-carbon emissions Machine-learning Net-zero building application Photovoltaics Sustainable building Sustainable energy Toronto building carbon emission energy efficiency machine learning photovoltaic system sustainability Hybrid systems Nur-E-Alam M. Zehad Mostofa K. Kar Yap B. Khairul Basher M. Aminul Islam M. Vasiliev M. Soudagar M.E.M. Das N. Sieh Kiong T. Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
description	The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energy-efficient and sustainable buildings, aiming for net-zero emissions. This research proposes a hybrid approach combining conventional solar panels with advanced solar window systems and building integrated photovoltaic (BIPV) systems. By analyzing the meteorological data and using the simulation models, we predict energy outputs for different cities such as Kuala Lumpur, Sydney, Toronto, Auckland, Cape Town, Riyadh, and Kuwait City. Although there are long payback times, our simulations demonstrate that the proposed all-PV blended system can meet the energy needs of modern buildings (up to 78%, location dependent) and can be scaled up for entire buildings. The simulated results indicate that Middle Eastern cities are particularly suitable for these hybrid systems, generating approximately 1.2 times more power compared to Toronto, Canada. Additionally, we predict the outcome of the possible incorporation of intelligent and automated systems to boost overall energy efficiency toward achieving a sustainable building environment. ? 2024 The Author(s)
author2	57197752581
author_facet	57197752581 Nur-E-Alam M. Zehad Mostofa K. Kar Yap B. Khairul Basher M. Aminul Islam M. Vasiliev M. Soudagar M.E.M. Das N. Sieh Kiong T.
format	Article
author	Nur-E-Alam M. Zehad Mostofa K. Kar Yap B. Khairul Basher M. Aminul Islam M. Vasiliev M. Soudagar M.E.M. Das N. Sieh Kiong T.
author_sort	Nur-E-Alam M.
title	Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
title_short	Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
title_full	Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
title_fullStr	Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
title_full_unstemmed	Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
title_sort	machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
publisher	Elsevier Ltd
publishDate	2025
_version_	1825816033885159424
score	13.244109

Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings

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