Nur-E-Alam, Mohammad and Mostofa, Kazi Zehad and Yap, Boon Kar and Basher, Mohammad Khairul and Islam, Mohammad Aminul and Vasiliev, Mikhail and Soudagar, Manzoore Elahi M. and Das, Narottam and Kiong, Tiong Sieh (2024) Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings. Sustainable Energy Technologies and Assessments, 62. p. 103636. ISSN 2213-1388, DOI https://doi.org/10.1016/j.seta.2024.103636.
Full text not available from this repository.Abstract
The focus of this work is on the optimization of an all-photovoltaic hybrid power generation systems for energyefficient 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.
| Item Type: | Article |
|---|---|
| Funders: | BOLD Refresh Postdoctoral Fellowships (J510050002-IC-6 BOLDREFRESH2025-Centre), Dato' Low Tuck Kwong International Energy Transition Grant (202203005ETG), School of Science, Edith Cowan University, Australia, VIC 3000, Australia |
| Uncontrolled Keywords: | Hybrid energy system; Low carbon emission; Net-zero buildings applications; Photovoltaics; Sustainable energy |
| Subjects: | T Technology > TJ Mechanical engineering and machinery T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 12 Nov 2024 02:22 |
| Last Modified: | 12 Nov 2024 02:22 |
| URI: | http://eprints.um.edu.my/id/eprint/45771 |
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