Achieving net zero energy through behavioral analytics and fuzzy regression analysis: A living-lab based study

Net-zero energy homes (NZEHs) play a crucial role in shaping building energy policies and setting cutting-edge standards. They are also considered a promising avenue for achieving sustainability within the building and construction sectors. However, research reveals that the actual energy performance of such homes can fall short of the planned targets, thereby adversely impacting the sector’s carbon reduction goals. Predictive energy models can be a cost-effective solution to ensure building energy performance targets are achieved. However, it is difficult to generate highly accurate predictive models due to inherent uncertainties in the data collected, which include missing information, the interaction among competing parameters, and the variability of occupant behavior. In this regard, fuzzy-based analysis models have the capability to accommodate data complexities and enhance the accuracy of an energy model. This work introduces an energy performance analysis framework utilizing fuzzy logic to assess correlations between energy use and environmental parameters of NZEHs. The framework is developed, and its utility is illustrated through a case study of an existing NZEH in the Okanagan Valley, Canada. In this work, fuzzy-based models are developed using data collected from two distinct operational phases over a four-month period. The collected data included occupant numbers, temperature, relative humidity, CO 2 concentrations, and electrical energy usage. The results of pairwise correlation analysis revealed strong relationships between outdoor and indoor temperature (r = 0.89–0.91) and moderate correlations for humidity and CO 2 . Fuzzy regression models achieved over 90% data coverage and explained more than 92% of the variability in energy consumption, outperforming conventional regression models in accommodating behavioral and environmental uncertainty. By capturing data uncertainties, the fuzzy regression framework developed in this work can provide valuable insights and enhance the development of more robust and reliable predictive models. These findings provide practical, data-driven recommendations for architects, builders, and policymakers for optimizing of net-zero homes, which will advance residential energy management strategies. • Fuzzy models are developed to predict energy-environmental parameters relationships • Data is monitored for the net-zero Show Home vs. typical occupancy • Strong correlation was found between indoor carbon dioxide and outdoor temperature for Show Home • Fuzzy regression models explained more than 92% of the variability in energy consumption