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  • 1.
    Chokwitthaya, Chanachok
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Zhu, Yimin
    Department of Construction Management, Louisiana State University, Baton Rouge, United States.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Ontology for experimentation of human-building interactions using virtual reality2023In: Advanced Engineering Informatics, ISSN 1474-0346, E-ISSN 1873-5320, Vol. 55, article id 101903Article in journal (Refereed)
    Abstract [en]

    Scientific experiments significantly enhance the understanding of human-building interactions in building and engineering research. Recently, conducting virtual reality (VR) experiments has gained acceptance and popularity as an approach to studying human-building interactions. However, little attention has been given to the standardization of the experimentations. Proper standardization can promote the reusability, replicability, and repeatability of VR experiments and accelerate the maturity of this emerging experimentation method. Responding to such needs, the authors proposed a virtual human-building interaction experimentation ontology (VHBIEO). It is an ontology at the domain level, extending the ontology of scientific experiments (EXPO) to standardize virtual human-building interaction experimentation. It was developed based on state-of-the-art ontology development approaches. Competency questions (CQs) were used to derive requirements and regulate the development. Semantic Web technologies were applied to make VHBIEO machine-readable, accessible, and processable. VHBIEO incorporates an application view (APV) to support the inclusion of unique information for particular applications. The authors performed taxonomy evaluations to assess the consistency, completeness, and redundancy, affirming no occurrence of errors in its structure. Application evaluations were applied for investigating its ability to standardize and support generating of machine-readable, accessible, and processable information. Application evaluations also verified the capability of APV to support the inclusion of unique information.

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  • 2.
    Feng, Kailun
    et al.
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China; Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China; Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden.
    Chen, Shiwei
    Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Department of Civil, Environmental, and Natural Resources Engineering, Luleå University of Technology, Luleå, Sweden.
    Wang, Shuo
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China; Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China.
    Yang, Bin
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin, China.
    Sun, Chengshuang
    School of Urban Economics and Management, Beijing University of Civil Engineering and Architecture, Beijing, China.
    Wang, Yaowu
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China; Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, Harbin Institute of Technology, Harbin, China.
    Embedding ensemble learning into simulation-based optimisation: a learning-based optimisation approach for construction planning2023In: Engineering Construction and Architectural Management, ISSN 0969-9988, E-ISSN 1365-232X, Vol. 30, no 1, p. 259-295Article in journal (Refereed)
    Abstract [en]

    Purpose: Simulation-based optimisation (SO) is a popular optimisation approach for building and civil engineering construction planning. However, in the framework of SO, the simulation is continuously invoked during the optimisation trajectory, which increases the computational loads to levels unrealistic for timely construction decisions. Modification on the optimisation settings such as reducing searching ability is a popular method to address this challenge, but the quality measurement of the obtained optimal decisions, also termed as optimisation quality, is also reduced by this setting. Therefore, this study aims to develop an optimisation approach for construction planning that reduces the high computational loads of SO and provides reliable optimisation quality simultaneously.

    Design/methodology/approach: This study proposes the optimisation approach by modifying the SO framework through establishing an embedded connection between simulation and optimisation technologies. This approach reduces the computational loads and ensures the optimisation quality associated with the conventional SO approach by accurately learning the knowledge from construction simulations using embedded ensemble learning algorithms, which automatically provides efficient and reliable fitness evaluations for optimisation iterations.

    Findings: A large-scale project application shows that the proposed approach was able to reduce computational loads of SO by approximately 90%. Meanwhile, the proposed approach outperformed SO in terms of optimisation quality when the optimisation has limited searching ability.

    Originality/value: The core contribution of this research is to provide an innovative method that improves efficiency and ensures effectiveness, simultaneously, of the well-known SO approach in construction applications. The proposed method is an alternative approach to SO that can run on standard computing platforms and support nearly real-time construction on-site decision-making.

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  • 3.
    Feng, Kailun
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Penaka, Santhan Reddy
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Eklund, Erik
    Umeå Municipality, Sweden.
    Andersson, Staffan
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Energy-efficient retrofitting with incomplete building information: a data-driven approach2022In: E3S web of conferences / [ed] A. Li, T. Olofsson; R. Kosonen, EDP Sciences, 2022, Vol. 356, article id 01003Conference paper (Refereed)
    Abstract [en]

    The high-performance insulations and energy-efficient HVAC have been widely employed as energy-efficient retrofitting for building renovation. Building performance simulation (BPS) based on physical models is a popular method to estimate expected energy savings for building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. To address this challenge, this paper proposes a data-driven approach to support the decision-making of building retrofitting under incomplete information. The data-driven approach is constructed by integrating backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), principal component analysis (PCA), and trimmed scores regression (TSR). It is motivated by the available big data sources from real-life building performance datasets to directly model the retrofitting performances without generally missing information, and simultaneously impute the case-specific incomplete information. This empirical study is conducted on real-life buildings in Sweden. The result indicates that the approach can model the performance ranges of energy-efficient retrofitting for family houses with more than 90% confidence. The developed approach provides a tool to predict the performance of individual buildings from different retrofitting measures, enabling supportive decision-making for building owners with inaccessible complete building information, to compare alternative retrofitting measures.

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  • 4.
    Feng, Kailun
    et al.
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Wang, Yaowu
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Man, Qingpeng
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Energy-Efficient Retrofitting under Incomplete Information: A Data-Driven Approach and Empirical Study of Sweden2022In: Buildings, E-ISSN 2075-5309, Vol. 12, no 8, article id 1244Article in journal (Refereed)
    Abstract [en]

    The building performance simulation (BPS) based on physical models is a popular method to estimate the expected energy-savings of energy-efficient building retrofitting. However, many buildings, especially the older building constructed several decades ago, do not have full access to complete information for a BPS method. Incomplete information generally comes from the information that is missing, such as the U-value of part building components, due to incomplete documentation or component deterioration over time. It also comes from the case-specific incomplete information due to different documentation systems. Motivated by the available big data of real-life building performance datasets (BPDs), a data-driven approach is proposed to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach constructed a Performance Modelling with Data Imputation (PMDI) with integrated backpropagation neural networks, fuzzy C-means clustering, principal component analysis, and trimmed scores regression. An empirical study was conducted on real-life buildings in Sweden, and the results validated that the PMDI method can model the performance ranges of energy-efficient retrofitting for family house buildings with more than 90% confidence. For a target building in Stockholm, the suggested retrofitting measure is expected to save energy by 12,017~17,292 KWh/year.

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  • 5.
    Feng, Kailun
    et al.
    Dept. of Construction Management, Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Wang, Shuo
    Dept. of Construction Management, Harbin Institute of Technology, Harbin, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Liu, Changyong
    Dept. of Construction Management, Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Wang, Yaowu
    Dept. of Construction Management, Key Lab of Structures Dynamic Behavior and Control, Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Planning Construction Projects in Deep Uncertainty: A Data-Driven Uncertainty Analysis Approach2022In: Journal of construction engineering and management, ISSN 0733-9364, E-ISSN 1943-7862, Vol. 148, no 8, article id 04022060Article in journal (Refereed)
    Abstract [en]

    Construction planning is significantly affected by many uncertain factors derived from construction tasks, the environments, resources, technologies, personnel, and more. Uncertainty analysis approaches are thus critical to supporting the decision making associated with construction planning. However, the precise probability distributions (PDs) of uncertain factors are sometimes inaccessible, especially for construction projects in a novel context with limited previous experiences or similar references. These situations constitute a deep uncertainty problem, and probability-based methods are no longer applicable for construction planning. To address this challenge, an uncertainty analysis approach that integrates Latin hypercube sampling (LHS), discrete-event simulation (DES), and the patient rule induction method (PRIM) is proposed. Specifically, it is progressed by LHS and DES to generate a wide array of uncertainty scenarios represented by possible PDs to quantify the robustness of various construction decisions; then, PRIM is used to identify the vulnerable scenarios that will jeopardize project completion. The approach was implemented on a real-world project, and the results demonstrated that it was able to identify the most robust construction schemes and vulnerable scenarios for construction planning. This research contributes a data-driven technology that provides an uncertainty analysis approach for construction planning without relying on assumed probability distributions from limited, unreliable project references.

  • 6.
    Hu, Siying
    et al.
    Dept. of Construction Management, Harbin Institute of Technology, Harbin, China.
    Qiu, Shaowei
    Dept. of Construction Management, Harbin Institute of Technology, Harbin, China.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Man, Qingpeng
    Dept. of Construction Management, Harbin Institute of Technology, Harbin, China.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    A data-driven exploration of the relations between occupant behaviors and comfort performances of energy-efficient measures2023In: ICCREM 2023: the human-centered construction transformation - proceedings of the international conference on construction and real estate management 2023 / [ed] Yaowu Wang; Feng Lan; Geoffrey Q. P. Shen, American Society of Civil Engineers (ASCE), 2023, p. 592-604Conference paper (Refereed)
    Abstract [en]

    Energy-efficient building retrofitting plays a crucial role in reducing energy consumption and carbon emissions within the building sector. Energy-efficient retrofitting brings about changes in the built environment and it could influence the occupant behaviors. Additionally, occupant behaviors, in turn, alter the indoor environment, thereby affecting the comfort performance of the building after retrofitting. To explore this intricate relation between occupant behaviors and comfort performances of energy-efficient measures, this paper employs a data-driven approach to compile a comprehensive dataset encompassing occupant behaviors, energy-efficient measures, and associated indoor comfort of an office building in Umeå University, Sweden. Multiple binary logistic regression is applied to quantify the relationship between occupant behaviors and comfort performances of energy-efficient measures. The findings of this study hold significant value, providing guidance for occupants in adapting to energy-efficient measures while also informing future retrofitting implementation.

  • 7.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Surrogate models in machine learning for computational stochastic multi-scale modelling in composite materials design2022In: International Journal of Hydromechatronics, ISSN 2515-0464, Vol. 5, no 4, p. 336-365Article in journal (Refereed)
    Abstract [en]

    We propose a computational framework using surrogate models through five steps, which can systematically and comprehensively address a number of related stochastic multi-scale issues in composites design. We then used this framework to conduct an implementation in nano-composite. Uncertain input parameters at different scales are propagated within a bottom-up multi-scale framework. Representative volume elements in the context of finite element modelling (RVE-FEM) are used to finally obtain the homogenised thermal conductivity. The input parameters are selected by a top-down scanning method and subsequently are converted as uncertain inputs. Machine learning approaches are exploited for computational efficiency, where particle swarm optimisation (PSO) and ten-fold cross validation (CV) are employed for hyper-parameter tuning. Our machine learning prediction results agree well with published experimental data, which proves our computational framework can be a versatile and efficient method to design new complex nano-composites.

  • 8.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Hu, Xiaoyue
    Faculty of Architecture and Urbanism, Bauhaus-Universität Weimar, Weimar, Germany.
    Zhang, Chao
    Yellow River Laboratory, Zhengzhou University, Zhengzhou, China; Institute of Underground Engineering, Zhengzhou University, Zhengzhou, China; National Local Joint Engineering Laboratory of Major Infrastructure Testing and Rehabilitation Technology, Zhengzhou, China; Collaborative Innovation Center for disaster prevention and control of Underground Engineering jointly built by provinces and ministries, Zhengzhou, China.
    Wang, Cuixia
    Yellow River Laboratory, Zhengzhou University, Zhengzhou, China; Institute of Underground Engineering, Zhengzhou University, Zhengzhou, China; National Local Joint Engineering Laboratory of Major Infrastructure Testing and Rehabilitation Technology, Zhengzhou, China; Collaborative Innovation Center for disaster prevention and control of Underground Engineering jointly built by provinces and ministries, Zhengzhou, China.
    Qu, Yilin
    State Key Laboratory for Strength and Vibration of Mechanical Structures, Xi’an Jiaotong University, Xi’an, Shaanxi, China.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Multiscale modeling of thermal properties in Polyurethane incorporated with phase change materials composites: a case study2023In: Healthy buildings Europe 2023: beyond disciplinary boundaries, Red Hook, NY: Curran Associates, Inc., 2023, Vol. 2, p. 923-929Conference paper (Refereed)
    Abstract [en]

    Polyurethane (PU) is an ideal thermal insulation material due to its excellent thermal properties. The incorporation of Phase Change Materials (PCMs) capsules into Polyurethane (PU) has been shown to be effective in building envelopes. This design can significantly increase the stability of the indoor thermal environment and reduce the fluctuation of indoor air temperature. We develop a multiscale model of a PU-PCM foam composite and study the thermal conductivity of this material. Later, the design of materials can be optimized by obtaining thermal conductivity. We conduct a case study based on the performance of this optimized material to fully consider the thermal comfort of the occupants of a building envelope with the application of PU-PCMs composites in a single room. At the same time, we also predict the energy consumption of this case. All the outcomes show that this design is promising, enabling the passive design of building energy and significantly improving occupants' comfort.

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  • 9.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Multiscale modeling of Heat transfer in Polyurethane - Phase Change Materials composites2023In: Yound investigators symposium Umeå 2023: Book of abstracts & programme, Umeå: Umeå University , 2023, p. 29-29Conference paper (Refereed)
    Abstract [en]

    Polyurethane (PU) exhibits exceptional thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane (PU) has proven to be highly effective in enhancing building envelopes. This innovative design greatly enhances the stability of indoor thermal environments and reduces fluctuations in indoor air temperature. To investigate the thermal conductivity of this composite material, we have developed a comprehensive multiscale model of a PU-PCM foam composite. By obtaining thermal conductivity data, we can optimize the material's design for maximum effectiveness. To fully assess the thermal comfort of occupants within a building envelope, we have conducted a case study based on the performance of this optimized material. Specifically, we focused on a single room where PU-PCM composites were applied. Simultaneously, we predicted the energy consumption associated with this scenario. The results of our study clearly demonstrate the promising nature of this design, as it enables passive building energy design and significantly improves the comfort experienced by occupants.

  • 10.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Zhuang, Xiaoying
    Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany.
    Rabczuk, Timon
    Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Stochastic interpretable machine learning based multiscale modeling in thermal conductivity of Polymeric graphene-enhanced composites2024In: Composite structures, ISSN 0263-8223, E-ISSN 1879-1085, Vol. 327, article id 117601Article in journal (Refereed)
    Abstract [en]

    We introduce an interpretable stochastic integrated machine learning based multiscale approach for the prediction of the macroscopic thermal conductivity in Polymeric graphene-enhanced composites (PGECs). This method encompasses the propagation of uncertain input parameters from the meso to macro scale, implemented through a foundational bottom-up multi-scale framework. In this context, Representative Volume Elements in Finite Element Modeling (RVE-FEM) are employed to derive the homogenized thermal conductivity. Besides, we employ two sets of techniques: Regression-tree-based methods (Random Forest and Gradient Boosting Machine) and Neural networks-based approaches (Artificial Neural Networks and Deep Neural Networks). To ascertain the relative influence of factors on output estimations, the SHapley Additive exPlanations (SHAP) algorithm is integrated. This interpretable machine learning methodology demonstrates strong alignment with published experimental data. It holds promise as an efficient and versatile tool for designing new composite materials tailored to applications involving thermal management.

  • 11.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Penaka, Santhan Reddy
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Rebbling, Anders
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Data-driven quantitative analysis of an integrated open digital ecosystems platform for user-centric energy retrofits: A case study in northern Sweden2023In: Technology in society, ISSN 0160-791X, E-ISSN 1879-3274, Vol. 75, article id 102347Article in journal (Refereed)
    Abstract [en]

    This paper presents an open digital ecosystem based on a web-framework with a functional back-end server for user-centric energy retrofits. This data-driven web framework is proposed for building energy renovation benchmarking as part of an energy advisory service development for the Västerbotten region, Sweden. A 4-tier architecture is developed and programmed to achieve users’ interactive design and visualization via a web browser. Six data-driven methods are integrated into this framework as backend server functions. Based on these functions, users can be supported by this decision-making system when they want to know if a renovation is needed or not. Meanwhile, influential factors (input values) from the database that affect energy usage in buildings are to be analyzed via quantitative analysis, i.e., sensitivity analysis. The contributions to this open ecosystem platform in energy renovation are: 1) A systematic framework that can be applied to energy efficiency with data-driven approaches, 2) A user-friendly web-based platform that is easy and flexible to use, and 3) integrated quantitative analysis into the framework to obtain the importance among all the relevant factors. This computational framework is designed for stakeholders who would like to get preliminary information in energy advisory. The improved energy advisor service enabled by the developed platform can significantly reduce the cost of decision-making, enabling decision-makers to participate in such professional knowledge-required decisions in a deliberate and efficient manner. This work is funded by the AURORAL project, which integrates an open and interoperable digital platform, demonstrated through regional large-scale pilots in different countries of Europe by interdisciplinary applications.

  • 12.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Vu-Bac, Nam
    Zhuang, Xiaoying
    Institute of Photonics, Gottfried Wilhelm Leibniz Universität Hannover, Hannover, Germany.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Fu, Xiaolong
    Xi’an Modern Chemistry Research Institute, Xi’an, China.
    Rabczuk, Timon
    Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Al-DeMat: A web-based expert system platform for computationally expensive models in materials design2023In: Advances in Engineering Software, ISSN 0965-9978, E-ISSN 1873-5339, Vol. 176, article id 103398Article in journal (Refereed)
    Abstract [en]

    We present a web-based framework based on the R shiny package with functional back-end server in machine learning methods. A 4-tiers architecture is programmed to achieve users’ interactive design and visualization via a web browser. Many data-driven methods are integrated into this framework, namely Random Forest, Gradient Boosting Machine, Artificial and Deep neural networks. Moreover, a robust gradient-free optimization technique, the Particle Swarm Optimization, is used to search optimal values in hyper-parameters tuning. K-fold Cross Validation is applied to avoid over-fitting. R2 and RMSE are considered as two key factors to evaluate the trained models. The contributions to the expert system in materials design are: (1) A systematic framework that can be applied in materials prediction with machine learning approaches, (2) A user-friendly web-based platform that is easy and flexible to use and (3) integrated optimization and visualization into the framework with pre set algorithms. This computational framework is designed for researchers and materials engineers who would like to do the preliminary designs before experimental studies. Finally, we demonstrate the performance of the web-based framework through 2 case studies.

  • 13.
    Liu, Bokai
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Wang, Yizheng
    Department of Engineering Mechanics, Tsinghua University, Beijing, China; Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Rabczuk, Timon
    Institute of Structural Mechanics, Bauhaus-Universität Weimar, Weimar, Germany.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Multi-scale modeling in thermal conductivity of polyurethane incorporated with phase change materials using physics-informed neural networks2023In: Renewable energy, ISSN 0960-1481, E-ISSN 1879-0682, Vol. 220, article id 119565Article in journal (Refereed)
    Abstract [en]

    Polyurethane (PU) possesses excellent thermal properties, making it an ideal material for thermal insulation. Incorporating Phase Change Materials (PCMs) capsules into Polyurethane has proven to be an effective strategy for enhancing building envelopes. This innovative design substantially enhances indoor thermal stability and minimizes fluctuations in indoor air temperature. To investigate the thermal conductivity of the Polyurethane-Phase Change Materials foam composite, we propose a hierarchical multi-scale model utilizing Physics-Informed Neural Networks (PINNs). This model allows accurate prediction and analysis of the material’s thermal conductivity at both the meso-scale and macro-scale. By leveraging the integration of physics-based knowledge and data-driven learning offered by Physics-Informed Neural Networks, we effectively tackle inverse problems and address complex multi-scale phenomena. Furthermore, the obtained thermal conductivity data facilitates the optimization of material design. To fully consider the occupants’ thermal comfort within a building envelope, we conduct a case study evaluating the performance of this optimized material in a detached house. Simultaneously, we predict the energy consumption associated with this scenario. All outcomes demonstrate the promising nature of this design, enabling passive building energy design and significantly improving occupants’ comfort. The successful development of this Physics-Informed Neural Networks-based multi-scale model holds immense potential for advancing our understanding of Polyurethane-Phase Change Material’s thermal properties. It can contribute to the design and optimization of materials for various practical applications, including thermal energy storage systems and insulation design in advanced building envelopes.

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  • 14.
    Lu, Chujie
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.
    Gu, Junhua
    School of Artificial Intelligence, Hebei University of Technology, Tianjin, China; State Key Laboratory of Reliability and Intelligence of Electrical Equipment, Hebei University of Technology, Tianjin, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    An improved attention-based deep learning approach for robust cooling load prediction: public building cases under diverse occupancy schedules2023In: Sustainable cities and society, ISSN 2210-6707, Vol. 96, article id 104679Article in journal (Refereed)
    Abstract [en]

    Space cooling in buildings is responsible for massive energy consumption and carbon emissions. Accurate cooling load prediction can facilitate the implementation of energy-efficiency cooling control strategies in practice. In this paper, an improved attention-based deep learning approach is proposed for robust ultra-short-term cooling load prediction. First, a novel time representation learning is introduced to extract the periodicity and non-periodicity of cooling loads efficiently. Then, long short-term memory with an attention mechanism extracts properly the time steps by identifying the relevant hidden states and learns high-level temporal dependency. The approach additionally incorporates extreme gradient boosting through the error reciprocal method, enhancing the elimination of prediction errors and improving robustness. The study takes Guangzhou as an example and generates cooling loads using diverse occupancy schedules of five building types based on the Chinese National Standard and Typical Meteorological Year data. The approach is evaluated on datasets comprising the cooling loads, meteorological data, and contextual information. Through results analysis, the approach outperforms other models in terms of prediction accuracy and robustness across all building types. Additionally, model interpretation is provided regarding feature importance and attention matrixes, which enhances the understanding and transparency of the final prediction from the proposed approach.

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  • 15.
    Lu, Chujie
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. School of Computer Science and Technology, Guangdong University of Technology, Guangzhou, China.
    Li, Sihui
    College of Energy and Power Engineering, Changsha University of Science and Technology, Changsha, China.
    Gu, Junhua
    School of Artificial Intelligence, Hebei University of Technology, Tianjin, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Ma, Jianguo
    School of Micro-Nano Electronics, Zhejiang University, Hangzhou, China.
    A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners2023In: Journal of Building Engineering, E-ISSN 2352-7102, Vol. 64, article id 105602Article in journal (Refereed)
    Abstract [en]

    Integrating renewable energy is a promising solution for buildings to achieve the net-zero-energy goal. Expanding real-time matching between renewable energy generation and building energy demand can help realize more enormous zero-energy potential in practice. However, there are few studies to investigate the real-time energy matching in renewable energy building design. Therefore, in this study, a hybrid ensemble learning framework is proposed for analyzing and predicting zero-energy potential in the real-time matching of photovoltaic direct-driven air conditioner (PVAC) systems. First, the datasets of zero-energy probability (ZEP) are generated under the three main climate regions in China, which are with consideration of the load flexibility of air conditioners and based on six important design variables. Second, a novel ensemble learning method named Extreme Gradient Boosting (XGBoost) is selected to predict ZEP and the Bayesian Optimization (BO) is adopted to identify the optimal hyperparameters and further improve the prediction performance. The statistical analysis shows that ZEP distributions are very different from one region to another one and the PVAC systems in Beijing are the easiest to achieve the zero-energy goal. Among all the variables, PV capacity is the most significant and positively related to ZEP. The prediction results show BO-XGBoost achieves more than 99% accuracy and outperforms other benchmark models in the ZEP prediction of three cities. In a word, this paper reveals BO-XGBoost is the most effective model for ZEP prediction and provides the framework for designers to utilize zero-energy potential analysis and prediction for the first time.

  • 16.
    Man, Qingpeng
    et al.
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Yu, Haitao
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Feng, Kailun
    Department of Construction Management, Key Lab of Structures Dynamic Behavior and Control of the Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics. International Joint Laboratory on Low Carbon Built Environment, Ministry of Education of China, Xi'an University of Architecture and Technology, Xi'an, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Transfer of building retrofitting evaluations for data-scarce conditions: an empirical study for Sweden to China2024In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 310, article id 114041Article in journal (Refereed)
    Abstract [en]

    Evaluating and comparing the performances of different strategies is critical for energy-efficient building retrofitting. Data-driven modelling based on large building performance datasets is an effective method for such evaluations. However, it could be challenging to apply this approach to buildings from data-scarce areas where local building performance datasets have not been well-established, which means the data falls short of the high demand for building retrofitting on a global level. To address this, a transfer learning approach is proposed in this study that can evaluate the performance of buildings without local well-established building performance datasets. The proposed approach is applied in the Swedish-Chinese empirical study that relies on the Swedish dataset to transfer and predict the building performance in China without well-established datasets. It was achieved by applying fuzzy C-means clustering and a neural network (FCM-BRBNN) to pre-train the evaluation model based on the Swedish dataset. Then, the proposed approach collects a small sample of Chinese buildings in the data-scarce area and transfers the model to local building performance prediction. The results show that the transfer learning approach can reliably predict the performance of building retrofitting in data-scarce areas with only hundreds of local building samples. As such, this study provides a novel methodology that can support the evaluation and comparison of retrofitting strategies in data-scarce regions and countries with only limited local data. It could efficiently assist designers in optimizing energy-efficient designs in the pre-retrofit stage. Crucially, the methodology enables the transfer of knowledge regarding building performance across different countries and regions, being pivotal for the international collaboration required to stimulate the global energy-efficiency transformation.

  • 17.
    Penaka, Santhan Reddy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Rebbling, Anders
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Improved energy retrofit decision making through enhanced bottom-up building stock modelling2024In: Energy and Buildings, ISSN 0378-7788, E-ISSN 1872-6178, Vol. 318, article id 114492Article in journal (Refereed)
    Abstract [en]

    Modelling the performance of building stocks is crucial in facilitating the renovation at the building stock level. Bottom-up building stock modelling begins by detailing individual buildings and then aggregates them into stock level. Its primary advantage lies in capturing the inherent heterogeneity among distinct buildings, which enables tailored retrofitting. Naturally, this approach requires a comprehensive dataset with detailed building information such as geometry and envelope thermal properties. However, a common challenge is the incompleteness of available data in individual datasets. To address this, previous bottom-up studies have filled the missing data with representative or statistical data. Such practice could lead to homogeneous modelling of distinct buildings within the same statistical group. This limits the utilization of key ability of bottom-up building stock modelling in capturing heterogeneity, such as tailored retrofitting to explore potential retrofitting areas and strategies. To address this challenge of homogeneous modelling, we utilize data fusion framework for bottom-up building stock modelling, employing probabilistic record linkage and inverse modelling techniques to integrate multiple incomplete building performance datasets. This framework fills the missing data in one dataset with information from another, thus capturing inherent heterogeneity in the building stock. An empirical study was conducted in Umeå, Sweden, to investigate the framework's effectiveness by modelling building stock with various retrofitting strategies. This study contribution lies in enhancing bottom-up building stock modelling by capturing inherent heterogeneity, to provide tailored retrofitting solutions.

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  • 18.
    Penaka, Santhan Reddy
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Rebbling, Anders
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Azizi, Shoaib
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    A data-driven framework for building energy benchmarking and renovation decision-making support in Sweden2023In: SBE23-Thessaloniki: Sustainable built environments: Paving the way for achieving the targets of 2030 and beyond, Institute of Physics (IOP), 2023, article id 012005Conference paper (Refereed)
    Abstract [en]

    In Europe, the buildings sector is responsible for 40% of energy use and more than 30% of buildings are older than 50 years. Due to ageing, a large number of houses require energy-efficient renovation to meet building energy performance standards and the national energy efficiency target. Although Swedish house owners are willing to improve energy efficiency, there is a need for a dedicated platform providing decision-making knowledge for house owners to benchmark their buildings. This paper proposes a data-driven framework for building energy renovation benchmarking as part of an energy advisory service development for the Vasterbotten region, Sweden. This benchmark model facilitates regional homeowners to benchmark their building energy performance relative to the national target and similar neighbourhood buildings. Specifically, based on user input data such as energy use, location, construction year, floor area, etc., this model benchmarks the user's building performance using two benchmark references i.e., 1) Sweden's target to reduce buildings by 50% energy use intensity (EUI) by 50% by 2050 compared to the average EUI in 1995, 2) comparing user building with the most relevant peer group of buildings, using energy performance certificates (EPC) big data. Several building groups will be classified based on influential factors that affect building energy use. Hence, this benchmark provides decision-making supportive knowledge to homeowners e.g., whether they need to perform an energy-efficient renovation. In the future, this methodology will be extended and implemented in the digital platform to provide helpful insights to decide on suitable EEMs. This work is an integral part of project AURORAL aims to deliver an interoperable, open, and integrated digital platform, demonstrated by cross-domain applications through large-scale pilots in 8 regions in Europe, including Vasterbotten.

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  • 19.
    Puttige, Anjan Rao
    et al.
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Are radiators ready for the challenges of the future: a review of advancements in radiators2022In: E3S Web of Conferences / [ed] A. Li, T. Olofsson; R. Kosonen, EDP Sciences, 2022, Vol. 356, article id 03024Conference paper (Refereed)
    Abstract [en]

    Radiators play an important role in providing a comfortable and safe indoor environment while maintaining high-energy efficiency. In the perspective of future climate change with expected larger temperature fluctuations and the rapidly changing heat supply and demand, it is required that the current radiator technology is adaptable. The heat supply is changing towards a lower supply temperature to enable an increase in energy efficiency and an increase in the share of renewable energy. Simultaneously, both the heat supply and demand are expected to have more variations in the future. An additional concern that has come into more focus after the experience with the COVID 19 pandemic is the prevention of the spread of infection in indoor environments. Researchers have extensively studied several innovations in radiator technologies and their deployment that addresses these challenges. Some of the solutions available in the literature include floor heating, ceiling heating, ventilation radiator, stratum ventilation. Researchers have used advanced modeling and experimental techniques to understand how to deploy different types of radiator technologies. This review summarizes solutions in the literature that address these challenges and identifies knowledge gaps that need to be addressed. In particular, this study explores the gaps in knowledge of practical issues, such as the position of furniture and the position of people, which have received less attention in the literature. Research that addresses the effect of radiators on ventilation and a healthy indoor environment is also of particular interest in this review.

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  • 20.
    Yu, Haitao
    et al.
    Department of Construction Management, Harbin Institute of Technology, Harbin, China; Key Lab of Structures Dynamic Behavior and Control, The Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Feng, Kailun
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Penaka, Santhan Reddy
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Man, Qingpeng
    Department of Construction Management, Harbin Institute of Technology, Harbin, China; Key Lab of Structures Dynamic Behavior and Control, The Ministry of Education, Harbin Institute of Technology, Harbin, China.
    Lu, Weizhuo
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Olofsson, Thomas
    Umeå University, Faculty of Science and Technology, Department of Applied Physics and Electronics.
    Data-driven modelling of building retrofitting with incomplete physics: a generative design and machine learning approach2023In: Journal of Physics, Conference Series, ISSN 1742-6588, E-ISSN 1742-6596, Vol. 2654, article id 012053Article in journal (Refereed)
    Abstract [en]

    Building performance simulation (BPS) based on physical models is a popular method for estimating the expected energy savings from energy-efficient building retrofitting. However, for many buildings, especially older buildings, built several decades ago, an operator do not have full access to the complete information for the BPS method. Incomplete information comes from the lack of detailed building physics, such as the thermal transmittance of some building components due to the deterioration of components over time. To address this challenge, this paper proposed a data-driven approach to support the decision-making of building retrofitting selections under incomplete information conditions. The data-driven approach integrates the backpropagation neural networks (BRBNN), fuzzy C-means clustering (FCM), and generative design (GD). It generates the required big database of building performance through generative design, which can overcome the problem of incomplete information during building performance simulation and energy-efficient retrofitting. The case study is based on old residential buildings in severe cold regions of China, using the proposed approach to predict energy-efficient retrofitting performance. The results indicated that the proposed approach can model the performance of residential buildings with more than 90% confidence, and show the variation of results. The core contribution of the proposed approach is to provide a way of performance prediction of individual buildings resulting from different retrofitting measures under the incomplete physics condition.

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