Development of a Collective Intelligence Framework for Optimising Demand and Supply-Side Smart Energy Management to Support Net-Zero Energy Outcomes in the Built Environment

Abstract

Achieving net-zero energy in residential precincts remains challenging due to diverse energy demand patterns, intermittent renewable energy generation, and the need to balance energy demand, supply, thermal comfort, and affordability. Existing studies have focused on optimising demand and supply to reduce grid energy consumption and cost, but have given limited attention to the emergence of collective intelligence through real-time interactions among humans, smart appliances, and battery energy storage systems across precincts to support the achievement of net-zero energy outcomes. Furthermore, collective knowledge sharing among these elements remains underexplored. This research develops a collective intelligence framework for optimising demand- and supply-side energy management to support net-zero energy outcomes at the precinct level. The framework consists of: (1) the modelling of a society of intelligent agents representing humans, smart appliances, and battery energy storage systems distributed across the precinct; and (2) a Collective Transfer Reinforcement Learning (CTRL) methodology integrated with the society of intelligent agents to enhance collective intelligence. The CTRL methodology includes three modules: a Discovery Module for discovering successful energy-use experiences that map onto high-performance outcomes; an Advisory Module for transferring successful experiences among agents; and a Diversity Module for preventing over-reliance on transferred experiences and avoiding suboptimal solutions. The developed collective intelligence framework was applied in two case study precincts. The results show a near net-zero energy outcomes while maintaining thermal comfort, effectively utilising solar energy, and reducing peak electricity demand. Compared with optimisation methods without collective intelligence, it reduced total net energy consumption by 28% and grid electricity costs by 8.6% at the precinct level. The study develops a multidisciplinary approach that integrates the built environment and energy systems domains, along with advanced artificial intelligence techniques, to tackle grand challenges in achieving net-zero carbon goals. Unlike conventional approaches, the novel collective intelligence framework developed in this thesis enables collective intelligence to emerge through interactive learning and experience sharing among intelligent agents, thereby supporting net-zero energy outcomes at the precinct level.