The Intelligent Human-Building Interaction Lab - A platform to investigate energy efficiency measures

The Umeå University established the Intelligent Human-Buildings Interaction (IHBI) Lab as a platform to explore energy efficiency measures in buildings. This platform allows occupants to experience various energy-saving strategies both virtually (e.g., visual appearances) and physically (e.g., temperature sensations) before they are implemented in real life. In a pilot study conducted in a campus office building, researchers successfully gathered data on occupants’ perceptions and behaviours under specific energy conditions. These insights be valuable for comparing different energy measures and informing real applications.

More than 40% of European buildings were built before 1960 – a period characterized by poor insulation standards for building envelopes. Nearly 75% of these older buildings can be considered not energy efficient and more or less rely on fossil fuels for space heating, often using outdated appliances [1]. Today, many of these buildings are in urgent need of renovation. Implementing energy efficiency measures is a promising way to reduce energy use and lower CO₂ emissions [2].

In 2020, the European Commission launched the Renovation Wave Action, planning to renovate 35 million European buildings within the ten years [3]. This initiative has the potential to reduce the energy use by 14% and CO₂ emissions by up to 60% by 2030.

The Renovation Wave Action offers a significant opportunity for the entire building industry – from manufacturers to service providers – to contribute to the energy transition. However, a key question remains: how will the proposed energy measures perform in practice?

While computer-aid simulations can estimate factors such as energy performance [4], they may not help people to have real perceptions before actual renovations. This gap may discourage the building users, known as occupants, from applying and accepting renovations, especially personal habits, daily schedules, and other needs from buildings are different. Therefore, it will be ideal to allow occupants to experience and test measures in advance of real-world implementation.

A research team at the Department of Applied Physics and Electronics, Umeå University [5], has recently created the Intelligent Human-Buildings Interaction (IHBI) Lab - a platform designed to explore and evaluate energy efficiency measures. The lab integrates virtual reality (VR) with a climate chamber technology, enabling occupants to engage with the virtual model almost as if they were in a real life – for example, by controlling windows, doors, and thermostats. Simultaneously, the climate chamber allows occupants to physically feel changes in temperature, humidity, and airflow that replicate the effects of renovation. In this way, the platform allows occupants to test different energy measures before they are implemented in actual buildings.

To demonstrate the platform, the research team used an office building at Umeå University, Teknikhuset building. As the building is operated over 30 years and is reported cold in winter, the building is a candidate for further renovation. Researchers working in the building were invited to investigate several energy efficiency measures.

The platform set up and preliminary findings are presented in the following sections. t is shown that the developed platform can be a valuable tool for comparing and testing energy measures.

Platform set up

The IHBI Lab integrates virtual reality and a climate chamber into a single cohesive system (see Figure 1). First, the VR component creates a digital model of a building allowing users to explore how different renovation options would work. Simultaneously, the climate chamber functions as a test environment, equipped with smart sensors and HVAC systems, that replicate indoor conditions – such as temperature, humidity, and airflow – after energy efficiency measures are applied. Together, these systems immerse users in both a virtual and a physical environment, allowing them to explore and feel the changes. This setup helps identify the most energy efficiency measures.

Figure 1. Overview of the IHBI Lab setup.

Virtual reality set up

The developed virtual reality model involves four main steps: creating virtual environment, simulating renovation measures, embedding preference vote, and enabling interactive functions. Figures 2a and 2b show a typical office room from the target building at Umeå University, where the room’s layout, furniture textures, and facilities are accurately replicated in the virtual space.

A built-in voting system (see Table 1) allows the occupants to report their current thermal sensation (TSV) and comfort level (TAV) under different renovation measures (Figure 2c). Additionally, occupants can interact with the building by performing actions such as turning a personal heater on or off, opening or closing doors, and adjusting clothing settings, as shown in Figure 2d.

Table 1. Rating Scale for Thermal Sensationx (TSV) and Thermal Acceptability (TAV).

Climate chamber development

The climate chamber is equipped with several devices (see Figure 3). It features a central HVAC system with both heating and cooling units, as well as an integrated humidifier. Multiple sensors continuously monitor temperature, humidity, and airflow to accurately simulate indoor conditions after renovation.

Data from user interactions in the VR model are transmitted in real time to control the climate chamber. Feedback from the sensors adjusts the system to reach the target conditions. The VR model and the climate chamber communicate via a programmable logic controller (PLC) and the Transmission Control Protocol (TCP), ensuring reliable and steady data transfer.

Test process and results

Test process and energy measures

The general test follows s outlined in Figure 4. For this test, 16 staff members and doctoral students were invited to use the IHBI Lab and evaluate different energy measures. All participants were healthy and were screened for VR sickness during the welcome and introduction section. Each participant experienced a simulated workday (from 8:00 to 17:00) under one specific energy measure while their interactions were recorded.

Four energy measures suitable for northern Sweden were tested: (1) external wall insulation, (2) window improvement, (3) Ventilation upgrading with heat recovery, (4) district heating adjustment (see Table 2 for details).

Figure 4. The test process for various energy measures.

Table 2. Energy efficiency measure details.

Note: “U” corresponds to the heat transfer coefficient, W/(m²*K).

Preliminary results

Early results show that occupants provided thermal feedback on all four energy measures. In terms of thermal sensation (as shown in Figure 5a), Ventilation Update produced the most significant improvement in occupants’ perceived warmth. In contrast, without any energy measures, thermal sensation remained low, while the other three measures brought moderate improvements. Regarding thermal acceptability, the differences were less pronounced. However, the District Heating Adjustment yielded the highest score, and the Ventilation Update also showed a noticeable improvement compared to having no energy measures (as shown in Figure 5b).

Additional observations included measurements of personal heater use (2000W), jacket-wearing, and door closing due to the cold temperatures (see Table 3). Without any energy measures, over 53% of subjects used personal heaters. When energy measures were applied, the Ventilation Update reduced heater use by up to 50%, while other measures achieved around a 20% reduction on average. Similarly, with the Ventilation Update, participants wore jackets less frequently. In cases without energy measures, office doors were closed more often, but the Ventilation Update reduced door-closing frequency by 30%.

In summary, the IHBI Lab can predict how occupants will behave under different energy measures. These insights can guide decision-making on which measures to implement in real building renovations.

Table 3. Office use behaviours under various renovation scenarios.

Conclusion

The preliminary results from the IHBI Lab are promising. The platform effectively simulates a range of energy efficiency measures in both virtual (see Figure 4) and physical settings (see Table 3). It provides a preview of how a building might look and feel after a retrofit, including changes in interior appearance, interactive features, and indoor climate conditions such as temperature and humidity. This immersive experience allows occupants to assess e both their comfort levels and interaction with the building before any real changes are made.

These insights gathered from this platform are valuable for both building owners and users when selecting the energy measures that best meet their needs. Additionally, these findings can also boost the adoption of such measures. Local energy advisors and related service providers, including SMEs, could adopt similar approaches to enhance current building renovation services. By offering people to “test” the building experience in advance - such as through improved thermal comfort and behavior feedback - this approach builds confidence and encourages actions towards renovations.

Acknowledgment

The authors would like to acknowledge the Formas project Buildings Post Corona (2021-02382), Formas project Intelligent Human-Buildings Interactions Lab (2022-01475), the Swedish Energy Agency (P2022-00141), and the EU JPI Urban Europe (BTC-ENUTC, project Envision Change) for financial support.

References

[1]     F. Filippidou and J. P. Jimenez Navarro, Achieving the cost-effective energy transformation of Europe’s buildings: combinations of insulation and heating & cooling technologies renovations: methods and data. in JRC technical report. Luxembourg: Publications Office of the European Union, 2019. doi: 10.2760/278207.

[2]     M. Karmellos, A. Kiprakis, and G. Mavrotas, “A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies,” Applied Energy, vol. 139, pp. 131–150, Feb. 2015, doi: 10.1016/j.apenergy.2014.11.023.

[3]     European_Commission, “A Renovation Wave for Europe - greening our buildings, creating jobs, improving lives.” 2020.

[4]     K. Feng, W. Lu, and Y. Wang, “Assessing environmental performance in early building design stage: An integrated parametric design and machine learning method,” Sustainable Cities and Society, vol. 50, p. 101596, Oct. 2019, doi: 10.1016/j.scs.2019.101596.

[5]     “Intelligent Human Buildings Interaction Iab.” [Online]. Available: https://www.umu.se/en/research/groups/intelligent-human-buildings-interaction-lab/.