People spend most of their time indoors, whether at home, at workplaces, or in public buildings. Poorly maintained indoor environment during occupied periods can lead to various health issues and discomfort, affecting day-to-day activities. Heating, Ventilation, and Air Conditioning (HVAC) systems play a crucial role in controlling these conditions. However, they consume a substantial amount of energy, up to 60% depending on building type. As the climate crisis intensifies and policies become stricter, traditional HVAC control strategies fail to meet expected goals.

With recent advancements in AI, this project aims to integrate AI into HVAC systems by automating and refining control strategies based on real-time data and learned patterns to make them more efficient. Literature gaps have been identified in the empirical integration of AI solutions, as well as their scalability and integration with existing Building Management Systems (BMS). To address these challenges, the project will use the West Park Teaching Hub at Loughborough University as a case study. This research will develop scalable, self-adaptable AI control strategies for optimisation of HVAC performance and test them in a real-world environment. By doing so, the project aims to contribute to the ongoing research in developing smart, low-carbon buildings.