Document Type

Journal Article

Publication Title

Sustainable Energy Technologies and Assessments






School of Science


Tenaga Nasional Berhad (TNB) and UNITEN through the Ministry of Higher Education of Malaysia / iRMC of Universiti Tenaga Nasional (UNITEN) / OLD Refresh Postdoctoral Fellowship / Centre of Excellence and Dato' Low Tuck Kwong International Energy Transition Grant


Nur-E-Alam, M., Mostafa, K. Z., Yap, B. K., Basher, M. K., Islam, M. A., Vasiliev, M., . . . Kiong, T. S. (2024). Machine learning-enhanced all-photovoltaic blended systems for energy-efficient sustainable buildings. Sustainable Energy Technologies and Assessments, 62, article 103636.


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.



Creative Commons License

Creative Commons Attribution 4.0 License
This work is licensed under a Creative Commons Attribution 4.0 License.