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

Sultana S.R. Rana A. Bakhtavar E. Hewage K. Alam M.S. Sadiq R.
15 November 2025 Elsevier Ltd

Journal of Building Engineering
2025 #114

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 sectors 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, CO₂ 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 CO₂. 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.

Dwellings , Fuzzy regression , Net-zero energy home , Open house , Post occupancy

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School of Engineering, The University of British Columbia, Kelowna, V1V 1V7, BC, Canada
National Research Council Canada, Ottawa, K1A 0R6, ON, Canada
Bharti School of Engineering & Computer Science, Laurentian University, Sudbury, P3E 2C6, ON, Canada
Nazarbayev University, Nur-Sultan, Z05H0P9, Kazakhstan

School of Engineering
National Research Council Canada
Bharti School of Engineering & Computer Science
Nazarbayev University

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