Achieving Domestic Energy Efficiency using Micro-Moments and Behavioural Economics-BasedRecommender Systems
Domestic consumer behaviour is a critical driver of energy use, igniting innovations that analyse and help establish energy-efficient behaviour. The most critical step is therefore the understanding of that behaviour by collecting and analysing comprehensive information. This does not only require financial and technological resources, but a strong understanding of human behaviour and motivation, i.e., the study of behavioural economics. Therefore, in this research, we tackle this very endeavour by utilising innovative artificial intelligence(AI) techniques to automatically process end-user information on consumption behaviour, decern outliers,and generate personalised recommendations to resolve energy wastage issues.Another critical building block is the concept of micromoments,defined as short, contextual events,comprising a specific end-user behaviour. Adopted to the energy context, micro-moments represent an informational building block of a consumption action and can be considered as the missing link between behavioural economics and artificial intelligence systems. In terms of impact, urban living standards can significantly benefit from the outcomes and tools of such innovations, in addition to, the immense social value gained from utilising recommender systems in reducing energy consumption to the convenient minimum. To conclude, vast positive impact can be created when innovation meets technology, in other words, when behavioural economics, micro-moments, recommenders combined to foster transforming the epicentre of energy efficiency challenges: the behaviour of individuals.