Quantifying Occupants’ Energy Behaviour: A Novel Fuzzy Approach

Buildings contribute to nearly 40% of the global carbon footprint with a significant proportion of their carbon footprint is generated due to their energy consumption during the operational phase. To reduce the carbon footprint of buildings, efforts have been made to optimize energy consumption during the operational phase; and the fundamental requirement for all such efforts is the accurate prediction of a building's energy consumption. Existing energy prediction models for buildings often map a set of building characteristics to their energy consumption and disregard the occupants’ energy behaviour (OEB). Consequently, significant discrepancies (up to 300%) have been resulted between the predictions of these models and the actual energy consumption values, which in turn reduce the applicability of these models in practice. This limitation can be addressed by incorporating OEB in buildings’ energy prediction models; however, OEB is an ill-known phenomenon under the impact of several factors, namely, the intra-personal (e.g., education, culture) and inter-personal (i.e., social interactions) characteristics of occupants, as well as environmental factors (e.g., precipitation). This paper introduces a novel insight into the energy behaviour of the occupants of buildings and provides a comprehensive and quantitative measure for modelling OEB based on granular computing and fuzzy logic. Our proposed fuzzy approach can improve the accuracy of existing energy prediction models by incorporating OEB into these models and so help with the energy consumption optimisation of buildings.