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

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posted on 2024-07-05, 04:25 authored by M Nur-E-Alam, K Zehad Mostofa, B Kar Yap, M Khairul Basher, M Aminul Islam, M Vasiliev, MEM Soudagar, Narottam DasNarottam Das, T Sieh Kiong
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.

History

Volume

62

Start Page

1

End Page

11

Number of Pages

11

ISSN

2213-1388

Publisher

Elsevier

Additional Rights

CC BY

Language

en

Peer Reviewed

  • Yes

Open Access

  • Yes

Acceptance Date

2024-01-14

Author Research Institute

  • Centre for Intelligent Systems

Era Eligible

  • Yes

Journal

Sustainable Energy Technologies and Assessments

Article Number

103636