Behavioural Modelling for Sustainability in Smart Homes

This paper explores the critical aspects of behavioural modelling for sustainability in smart homes, emphasising the integration of advanced technologies to enhance energy efficiency, convenience, security, and overall quality of life. Smart homes utilise interconnected devices and sensors to collect detailed data on residents' behaviours, energy usage, and environmental conditions. Predictive analytics, leveraging machine learning algorithms and data mining techniques, optimises home systems by anticipating residents' needs and promoting efficient energy use. Behavioural interventions, such as real-time feedback, automation, incentives, and nudges, influence residents' actions toward more sustainable practices. However, privacy concerns about data collection, unauthorised access, and data misuse present challenges. Addressing these issues through robust security measures, transparent policies, and user education is crucial. Additionally, promoting adoption and user engagement requires highlighting the perceived benefits, affordability, ease of use, and trusted brands while overcoming barriers like privacy concerns, technological complexity, and lack of awareness. Practical strategies to enhance adoption and engagement include education campaigns, financial incentives, user-friendly design, robust customer support, and community building. By addressing these factors, smart home technologies can become integral to modern living, contributing to a more sustainable and efficient future. This paper aims to provide a comprehensive overview of the current and future directions in smart home sustainability, highlighting the interplay between technology, policy, and user engagement to shape research directions and foster a sustainable and efficient future.