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Most existing and new buildings adapt poorly to variable occupancy, in part due to technical constraints and common operational practice. Although building automation systems and advanced control strategies aim to address this issue by improving adaptability to partial occupancy, no holistic metrics exist to quantify this aspect of building performance. To this end, we present a technology-independent approach to define adaptability as a building performance attribute and introduce metrics to quantify it. These metrics can be used to evaluate how different building technologies and control strategies influence building operations’ adaptability to variable occupancy or estimate their associated energy savings. To demonstrate these metrics, a case study based on simulating a single-story office building was used to compare several control strategies with regards to their effect on improving adaptability. Results showed how the proposed metrics highlighted the additional benefits of these control strategies, especially under low-occupancy scenarios. Performance-based compliance with building energy codes and standards typically assumes full or near full occupancy, which may underestimate the benefits of adaptable building technologies or controls. Therefore, incorporating adaptability metrics in energy codes and operational guidelines would quantify the benefits of adaptable systems, especially under variable occupancy.

Adapting building systems' operation to occupancy variations can provide significant energy savings, but this is typically constrained by the unavailability of occupancy information. Although several technologies have been introduced to integrate real-time occupancy information in heating, ventilation and air-conditioning (HVAC) systems operations (e.g., CO2 monitoring), the logistical and cost issues associated with deploying these technologies remained a key issue. Previous studies proposed using WiFi counts as a proxy for occupancy in building operations and showed a strong correlation between occupancy and WiFi counts in several building types. However, the difficulty of integrating real-time WiFi traffic data in building automation systems hinders wide-scale deployment of this approach. To this end, this study proposes a framework for extracting occupancy indicators from WiFi traffic data. The proposed framework utilizes several machine learning algorithms and statistical analysis methods to predict patterns of building occupancy as well as to identify peak occupancy time and earliest/latest arrival and departure times. To validate the proposed framework, it was implemented in a case-study using data collected from an academic building in Montreal, Canada between January and March 2020. Results revealed that the proposed models could successfully predict weekly building occupancy patterns, with an average accuracy (R2D) of 0.98 for weekdays and 0.81 for weekends. Furthermore, the analysis identified peak occupancy timing, as well as arrival and departure times variations between different zones. These findings provided a proof-of-concept for the proposed framework and demonstrated its potential to provide actionable information to modify the sequences of operation of building systems based on buildings’ unique occupancy patterns.

Occupants' behavior play the main role in the timing and magnitude of residential energy demand. However, obtaining a reliable source of data and developing representative models for these activities remain a key challenge, especially for developing energy models with high temporal resolution. In this context, the Time Use Survey (TUS) represents a promising solution for these data and modelling issues. This paper presents a comprehensive review of the TUS’ available data, modelling methods, and implementations in building energy research. The paper includes a detailed comparison between the available TUS across different countries and a description of additional surveys integrated with TUS to model occupants' activities and energy consumption. Further, the main TUS-based models are categorized and explained in detail including their main features and limitations. The implementation of these TUS-based models and TUS in residential energy modelling is reviewed through four main applications (lighting demand, domestic water heating, space cooling and heating, and demand-side management). Finally, the limitations and assumptions of the reviewed TUS data and models are presented and discussed along with highlighting the main research gaps and future opportunities.

The proliferation of urban sensing, IoT, and big data in cities provides unprecedented opportunities for a deeper understanding of occupant behaviour and energy usage patterns at the urban scale. This enables data-driven building and energy models to capture the urban dynamics, specifically the intrinsic occupant and energy use behavioural profiles that are not usually considered in traditional models. Although there are related reviews, none investigated urban data for use in modelling occupant behaviour and energy use at multiple scales, from buildings to neighbourhood to city. This survey paper aims to fill this gap by providing a critical summary and analysis of the works reported in the literature. We present the different sources of occupant-centric urban data that are useful for data-driven modelling and categorise the range of applications and recent data-driven modelling techniques for urban behaviour and energy modelling, along with the traditional stochastic and simulation-based approaches. Finally, we present a set of recommendations for future directions in data-driven modelling of occupant behaviour and energy in buildings at the urban scale.