Energy AI-Driven Optimisation in Green Buildings

The growing global concern over energy consumption and climate change has prompted a strong shift toward renewable sources. The building sector is one of the most significant contributors to energy consumption and greenhouse gas emissions, primarily due to energy demands for heating, cooling, and electricity. In countries such as Australia and India, residential and commercial buildings contribute significantly to national carbon emissions, with continued reliance on fossil fuels compounding the issue.;To mitigate these impacts, there is an urgent need to develop alternative energy strategies that reduce dependence on fossil fuels while maintaining indoor comfort and system efficiency. One promising solution involves designing integrated energy systems that combine renewable energy sources with waste heat recovery (WHR) technologies. Hybrid systems incorporating WHR have shown strong potential to reduce energy consumption, lower emissions, and minimise operational costs. Recent research indicates that integrating WHR into building energy systems can substantially enhance energy efficiency and reduce environmental footprints. ;This PhD project proposes to develop an advanced energy optimisation framework for residential and commercial buildings through the integration of renewable energy sources, WHR systems, and AI-driven control strategies. The research will explore the integration of renewable sources such as solar, wind and green hydrogen while also leveraging artificial intelligence to dynamically manage system performance for maximum efficiency.;The key objectives of this research are to investigate the potential of waste heat recovery (WHR) in building energy systems, particularly in conjunction with renewable energy sources. The next objective is to develop and simulate advanced combined cooling, heating, and power systems that integrate WHR and renewable technologies to enhance energy efficiency. A comprehensive life cycle assessment and economic analysis will also be conducted to evaluate the environmental and financial benefits of the proposed systems. Finally, artificial intelligence (AI)-based optimisation techniques will be employed to identify the most efficient system configurations and operational strategies under varying environmental and usage conditions.;This interdisciplinary project is ideal for candidates with a background in mechanical engineering, energy systems, or a related field. It offers the opportunity to contribute to cutting-edge research in sustainable building technologies, with direct relevance to national and global efforts to combat climate change.