Research group Intelligent Human-Buildings Interactions lab (IHBI) at Department of Applied Physics and Electronics, 91传媒在线, aims to explore the interaction among energy, energy-efficient measures and occupant behaviour using lab experiment. As a new research domain, IHBI conducts cutting-edge research on immersive built environment, data-driven modelling and machine learning, intelligent indoor environment for evidence-informed decision-making.
The building sector in the European Union (EU) is the largest consumer of energy across Europe and account for as much as 40% of the energy consumption. Today, roughly 75% of the EU building stock is energy inefficient and the energy-saving potential of the building sector is remarkable. Improving energy efficiency in buildings therefore has a key role to play in achieving the ambitious goal of carbon-neutrality by 2050, set out in the European Green Deal. For this reason, energy-efficient measures, such as efficient insulations in the building envelope, more energy-efficient windows, adoption of energy-efficient Heat Ventilation Air Conditioning (HVAC) system are being increasingly implemented.
Case studies have observed the significant discrepancy between actual and expected target of energy-consumption caused by improved energy efficiency. Energy-efficient measures alone do not guarantee efficient energy use in buildings. Indeed, energy related occupant behaviors, includes adjusting thermostat settings, opening/closing windows, dimming/switching lights, pulling up/down blinds, turning on/off HVAC systems have a considerable impact on building energy performance.
In fact, how occupant behaviours change under the influence of adoption of various energy-efficient measures remains an unresolved issue. IHBI is to bring a structured understanding of how these measures will influence occupant behaviour and how the changes in occupant behaviour will, in turn, impact expected energy savings caused by energy-efficient measures for achieving occupant-centric energy efficient.
A series of experiments are being conducted at IHBI lab to explore the interactions among energy, energy-efficient measures and occupant behaviour. Experiment is set up by adopting immersive built environment, intelligent indoor environment, big-data driven modelling and machine learning to build a hybrid virtual-physical experimental environment, as shown in Fig. below.
Publications
Penaka, S. R., Feng, K., Olofsson, T., & Lu, W. (2025). Diverse occupant behaviour and urban building heterogeneity to enhance urban building energy modelling. Energy and Buildings, 116721.
Feng, K., Chokwitthaya, C., & Lu, W. (2024). Exploring occupant behaviors and interactions in buildings with energy-efficient renovations: A hybrid virtual-physical experimental approach. Building and Environment, 265, 111991.
Penaka, S. R., Feng, K., Olofsson, T., Rebbling, A., & Lu, W. (2024). Improved energy retrofit decision making through enhanced bottom-up building stock modelling. Energy and Buildings, 318, 114492.
Man, Q., Yu, H., Feng, K., Olofsson, T., & Lu, W. (2024). Transfer of building retrofitting evaluations for data-scarce conditions: An empirical study for Sweden to China. Energy and Buildings, 310, 114041.
Liu, B., Penaka, S. R., Lu, W., Feng, K., Rebbling, A., & Olofsson, T. (2023). Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden. Technology in Society, 75, 102347.
Lu, C., Gu, J., & Lu, W. (2023). An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules. Sustainable Cities and Society, 96, 104679.
Chokwitthaya, C., Zhu, Y., & Lu, W. (2023). Ontology for experimentation of human-building interactions using virtual reality. Advanced engineering informatics, 55, 101903.
Eco-friendly light sources and climate-smart buildings recognized by Swedish academy.
