Research group Intelligent Human-Buildings Interactions lab (IHBI) at Department of Applied Physics and Electronics, Umeå University, 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
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, accepted
Liu, Bokai; Penaka, Santhan Reddy; Lu, Weizhuo; Feng, Kailun;Rebbling, Anders; Olofsson, Thomas
2023
Automated machine learning-based framework of heating and cooling load prediction for quick residential building design
Energy, Elsevier 2023, Vol. 274
Lu, Chujie; Li, Sihui; Penaka, Santhan Reddy; et al.
2023
Al-DeMat: A web-based expert system platform for computationally expensive models in materials design
Advances in Engineering Software, Elsevier 2023, Vol. 176
Liu, Bokai; Vu-Bac, Nam; Zhuang, Xiaoying; et al.
2023
An improved attention-based deep learning approach for robust cooling load prediction: public building cases under diverse occupancy schedules
Sustainable cities and society, Elsevier 2023, Vol. 96
Lu, Chujie; Gu, Junhua; Lu, Weizhuo
2023
Ontology for experimentation of human-building interactions using virtual reality
Advanced Engineering Informatics, Elsevier 2023, Vol. 55
Chokwitthaya, Chanachok; Zhu, Yimin; Lu, Weizhuo
2022
Energy-efficient retrofitting with incomplete building information: a data-driven approach
E3S web of conferences
Feng, Kailun; Lu, Weizhuo; Penaka, Santhan Reddy; et al.
2022
Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design
International Journal of Hydromechatronics, InderScience Publishers 2022, Vol. 5
Liu, Bokai; Lu, Weizhuo
Eco-friendly light sources and climate-smart buildings recognized by Swedish academy.
