Development of colletive intelligence for building energy efficiency

Energy consumption in the building sector is continuously increasing. In response to this situation, optimal collaborative action strategies aimed at improving building energy efficiency with human and building technical systems have become increasingly important. Collaborative actions which this research addresses focus on the interaction between humans and technical systems in a building environment. Most studies on building energy efficiency have dealt with the development of technical systems and lacked consideration of the complex socio-technological interface and collective efforts between technical systems and humans. This research aims to fill the gap by developing an innovative collective intelligence model to enable collective efforts by both building energy systems and people to achieve a greater energy saving. In this model, building energy systems and people are represented by intelligent agents, while genetic algorithms (GAs) are integrated into multi-agent modules to enable self-organization of energy efficient actions in order to achieve optimal energy consumption. The utility of the innovative collective intelligence model is further investigated through a multi-unit apartment building in the Australian context. As an example, the results of the prototype show that building energy performance can be significantly improved by using the proposed collective intelligence model compared to the baseline energy consumption of the building. This research links humans and collective intelligence with building energy systems to tackle energy efficiency problems in the built environment. Research outcomes will advance cross-disciplinary knowledge about the utilisation of artificial intelligence technologies for enhancing energy efficiency and sustainability in the built environment