Lab of Climate Responsive and
Intelligent Built Environment
—— CRIBE Lab
Creating smart-driven climate-resilient buildings and cities
OUR MISSION
Lab of Climate Responsive and Intelligent Built Environment (CRIBE Lab) looks for systematic approach that makes buildings and cities adapt responsively to changing climate conditions by incorporating smart technologies and design strategies that optimize energy usage, enhance comfort, and minimize environmental impact. The end of our research is to create human-made and human-centered surroundings that respond dynamically to changing weather and climate patterns while utilizing intelligent systems to manage energy consumption and physical environment effectively in buildings and cities.
—— Pengyuan Shen, Lab Founder, Associate Professor, Tsinghua SlGS
RESEARCH
Our lab focuses on advancing building performance, thermal comfort, and sustainability in the context of changing urban environments and climate dynamics.
Synergizing comfort and energy efficiency in the built environment
From the perspective of “human-centeredness”, this research area investigates how to synergize the comfort and energy efficiency of the built environment. The research proposes a real-time control strategy coupling building energy simulation (BES) and computational fluid dynamics (CFD), which realizes the dynamic optimization of comfort and energy consumption by comprehensively considering human thermal comfort, indoor environment non-uniformity and energy consumption [18]. Aiming at the balance between visual comfort and energy saving, a multi-objective optimal design method for external shading system was proposed, and the synergistic optimization of indoor light environment quality and building energy consumption was achieved by using an evolutionary algorithm [19]. A data-driven prediction model for human thermophysiological and psychological responses is also constructed, which outperforms the traditional model in dynamic thermal sensation prediction [21], providing an innovative solution for the refined management of the built environment.
Building energy prediction and management
This research direction aims to solve the problems of low efficiency of building energy simulation and lack of fine management of regional building energy consumption. The study proposes a lightweight building energy simulation engine, which innovatively introduces cross thermal zone heat transfer calculations into the resistive-capacitive (RC) model, reducing the computational intensity by an order of magnitude while ensuring a high degree of agreement with the results of authoritative simulation engines [16]. At the regional scale, the study incorporates global climate change and urbanisation factors into the regional building energy consumption prediction model [26], and proposes a bottom-up regional building energy consumption modelling and optimisation paradigm, which combines multi-source survey data with a physical model to achieve an accurate assessment of the energy saving potential of residential buildings in a large region [27].
Urban climate and building adaptation strategies
This research direction systematically explores future climate adaptive building energy saving and emission reduction strategies. At the urban climate level, the study proposes a method for generating future meteorological data coupling climate change and urban heat island effect, which provides accurate meteorological inputs for building adaptive design [2]. In terms of building adaptive strategies, the study innovatively assesses the impact of climate change on the performance of ground source heat pump systems, revealing the decrease in system energy efficiency due to global warming [29]. Meanwhile, the study also analyses the impact of climate change on renewable energy systems in net-zero energy buildings (NZEB) and proposes corresponding optimal allocation methods [30]. Taking Shenzhen as an example, the study modelled and analysed the performance of the regional energy system under future climate scenarios, revealing the potential risks of climate change on regional energy economics [31]. These studies provide key theoretical support for the development of future climate-adaptive building energy efficiency and emission reduction strategies.
Optimization and decision making for building energy efficiency strategies
This research direction is dedicated to providing a life cycle multi-objective optimization method and decision support system for building energy efficiency retrofit. The research pioneered a life cycle evaluation framework for building retrofit considering the impact of climate change, and realized the accurate screening of energy-saving measures by developing a parametric analysis tool and combining it with a feature selection method [9]. On this basis, an automatic optimization method for building energy efficiency retrofit is proposed, which integrates marginal abatement cost analysis, differential evolutionary algorithm, and decision tree-based process backtracking, breaking through the limitations of the traditional methods in parameter optimization and scheme generation, and realizing the acquisition of customized optimization schemes and decision backtracking [10]. Relevant studies also cover passive retrofit of traditional residential buildings [11], optimization of green roofs and natural ventilation for office buildings [12], etc., which provide systematic solutions for future climate-adaptive energy-saving retrofits in different climate zones and building types.
Building performance assessment under climate change
This research direction focuses on the dual impacts of climate change and urban heat island effect on building energy efficiency. By proposing a fast generation technique of urban microclimate data based on map crawling and clustering, the model prediction accuracy is significantly improved . Further, a parametric coupled simulation prediction method integrating Global Climate Models (GCMs) and the urban heat island effect at the city block scale is proposed, which significantly improves the accuracy of future microclimate prediction in typical cities such as Beijing, Shenzhen, and Shanghai [2]. In addition, the impacts of global climate change on building energy consumption in different climate zones [4, 5] and the role of neighborhood morphology on building energy consumption [8] have also been systematically investigated. These works have improved the science and accuracy of climate change impact assessment in the building sector through the development of multifactor coupled building energy consumption prediction methods and efficient data support technologies.
DEVELOPMENT
Building the tools for future discoveries
OUR TEAM
Highly skilled professionals, green building design research backgrounds in architecture & building science with industry and academic experience including computational and practical expertise
Join Our Lab
We are looking for passionate researchers to join our team. Currently recruiting excellent students in building science, thermal comfort and sustainability.
1 PhD student (Chinese mainland resident)
2 PhD student (non-Chinese mainland resident)
2 Postdocs
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