Conference Agenda

After the COVID-19 experience, employees showed interest in hybrid work as a new job style, even after the pandemic ended. One of the main sectors likely to be affected by this shift is residential buildings, as people spend more time at home, leaving them no longer vacant during office hours. To quantify the changes in energy use and greenhouse gas emissions resulting from this shift, simulations are needed to capture the various possible teleworking scenarios. One key factor influencing the results is occupant behavior regarding thermostat setpoints when they leave the house for extended periods. Additionally, understanding teleworkers' preferred setpoints and their usage of lighting and equipment are the key parameters in assessing the impact of telework on household energy consumption. To address these questions, we surveyed 5,662 employees who were teleworking at the time. The study findings revealed that setback/set-up users applied an average of a 2°C setback in winter and a similar set-up in summer. The results also indicated no significant difference between the setpoints used in the home office and the living room, suggesting that teleworkers did not change their setpoints while working. Furthermore, nearly 50% of participants reported no change in lighting usage during teleworking, while approximately 42% reported increased use of kitchen appliances when working from home. Moreover, to avoid the negative impact of partial occupancy and over-conditioning the empty rooms, we asked about participants’ willingness to pay for an upgrade. 28% declared they already upgraded and 26% reported they were interested in doing the upgrade.

This study examined how thermal adaptive behaviors in bedrooms, specifically air conditioning (AC) use, adjustments of sleepwear and bedding insulation, affect sleep quality and thermal comfort in tropical climates. Field measurements were conducted in the bedrooms of undergraduate and graduate students in two cities with different tropical climates: Bandung (Af) and Surabaya (Aw), Indonesia. Participants were mainly classified into three groups based on AC use and outdoor climate conditions: (1) AC users in Surabaya, (2) non-AC users in Surabaya and (3) non-AC users in Bandung. The results revealed that sleepwear insulation did not significantly differ between the AC and Non-AC groups in Surabaya, but the bedding insulation was higher in the AC group. In the Non-AC group, at temperatures above 28 °C, sleep efficiency was ensured with lower bedding insulation. In the AC group, although indoor temperatures were maintained between 24.5 °C and 26 °C, sleep efficiency was lower than that of the Non-AC group with similar bedding insulation. In the AC group, higher sleep efficiency was associated with greater bedding insulation. Moreover, thermal discomfort with cold-side sensations increased after sleep in the AC group, particularly below 26 °C, whereas thermal discomfort was less pronounced in the Non-AC group. These findings highlight the overlooked role of thermal adaptations during sleep in tropical climates and underscore the need for sustainable bedroom strategies to enhance sleep quality and comfort to counterbalance the increasing use of AC in this region.

Many asset rating systems adopt assumptions around the likely temperatures that homes are maintained at in order to determine the likely energy consumption of homes. This assumption could explain in part the difference in performance that is experienced between design and actual energy consumption values for heating. Furthermore, occupant satisfaction with the thermal environment is in part explained by allowing occupants control over their thermal environment. The following study utilises data from six to eight residential homes that have heat pumps and are in different geographic locations in Ireland. This study uses temperature data, energy performance certificates, manufacturers heat pump data and interviews with homeowners to determine what differences exist between design and reality and why this may be the case. Temperature data was collected in these homes between 2022 and 2024 and was compared with assumptions in energy performance certificates for both living and sleeping spaces. Current observations highlight a substantial difference between measurements and design assumptions. Recommendations are proposed as to how future performance certificates should be used to reflect these differences.

Wi-Fi sensing offers a cost-effective, non-intrusive method for understanding indoor environments, providing insights into where individuals are and what they are doing by leveraging existing Wi-Fi infrastructure. This study explores the use of Wi-Fi signals to detect occupant locations, activities, and interactions with the environment, such as opening windows or doors, in residential spaces. By analyzing variations in received signal strength indicator (RSSI) and channel state information (CSI), the system infers spatial dynamics and behavioral patterns without requiring intrusive devices or additional hardware. Controlled experiments were conducted in various residential layouts to evaluate performance. The methodology involves a combination of data collection from Wi-Fi-enabled devices and signal processing techniques to extract spatial and temporal features. Machine learning algorithms, such as convolutional neural networks (CNN), were used identify occupant locations with high accuracy. Results demonstrate capability to detect occupant locations. The study also highlights its robustness in scenarios with signal obstruction and coexisting wireless devices. Moreover, spatial dynamics inferred from Wi-Fi signals reveal patterns of space utilization, such as room occupancy rates, and identify airflow disruptions caused by window or door openings, which directly affect thermal comfort and air quality. This research underscores the potential of Wi-Fi sensing as a privacy-preserving, scalable tool for occupant-centric building management. By offering real-time insights into occupant behavior and interactions, this technology supports applications in adaptive energy systems, thermal comfort optimization, and indoor air quality enhancement, driving advancements in sustainable and responsive building design.

Energy poverty and poor IAQ disproportionately affect vulnerable populations. In Ireland, approximately 29% of households experience energy poverty, which is defined as spending 10% or more of their income on heating. Financial constraints often drive affected households to prioritise thermal comfort, opting for solid fuel heating, and limiting ventilation—behaviours that elevate indoor air pollutant concentrations.

This study examines the impact of energy poverty-driven occupant behaviours on IAQ, focusing on key parameters such as PM2.5, VOCs, CO, CO2, radon, and RH.

The study employs a mixed-methods approach using detailed questionnaires and interactive surveys to collect data from 1,000 participants. The questionnaires capture a wide range of socio-demographic variables, such as age, income, household size, and tenancy status, to contextualise the impact of energy poverty on diverse populations.

Interactive surveys are designed to provide real-time insights into occupant behaviours and decision-making processes regarding ventilation and heating. They feature adaptive questioning, allowing participants to elaborate on practices such as burning solid fuels, blocking or reducing ventilation, drying clothes indoors, and selective heating in specific rooms.

Occupant behaviour data will inform simulations using CONTAM, a dynamic building physics model that analyses airflow, temperature distributions, and pollutant dispersion in dwellings at risk of energy poverty. Simulated dwelling types—basement units, top-floor and ground-floor apartments, and terraced houses—reflect national census data on vulnerable housing. The simulations integrate survey insights to model the lived experiences of energy-poor households, focusing on energy use, ventilation strategies, and resultant impacts on IEQ. This approach uncovers the complex interplay between energy poverty, occupant Behaviour and indoor air quality identifying critical pathways of pollutant exposure. The study aims to provide actionable insights for policymakers to mitigate the adverse health impacts of energy poverty.