Development of data-driven methods to enhance building energy efficiency and indoor air quality

<p dir="ltr">Indoor Air Quality (IAQ) impacts people’s productivity and well-being and is showing a significant need to be improved in the post-Covid era due to people’s rising indoor time. The maintenance of high IAQ relies on Heating, Ventilation and Air Conditioning (HVAC) systems, which require a large amount of energy and the building energy efficiency is therefore becoming increasingly critical. Due to the recent development of the Internet of Things (IoT) and sensor technologies, a large amount of building data has been available and provided more opportunities to utilise data-driven methods to improve IAQ and building energy efficiency. This thesis aims to develop robust data-driven strategies to appropriately identify dynamic IAQ patterns and association rules that exist in building IAQ, energy use and HVAC operational data to comprehensively evaluate building IAQ and energy use performance and develop reliable and optimal control strategies for HVAC systems to enhance IAQ and minimise building energy consumption.</p>