Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings
Document Type
Article
Publication Date
2-1-2024
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.
Keywords
Hybrid energy system, Low carbon emission, Net-zero buildings applications, Photovoltaics, Sustainable energy
Divisions
sch_ecs
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
Publication Title
Sustainable Energy Technologies and Assessments
Volume
62
Publisher
Elsevier
Publisher Location
RADARWEG 29, 1043 NX AMSTERDAM, NETHERLANDS