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  • 1.
    Boussaa, Youcef
    et al.
    Department of Built Environment and Energy Technology, Linnaeus University, Växjö, Sweden.
    Dodoo, Ambrose
    Department of Building Technology, Linnaeus University, Växjö, Sweden.
    Truong, Nguyen Le
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Rupar-Gadd, Katarina
    Department of Built Environment and Energy Technology, Linnaeus University, Växjö, Sweden.
    Integrating passive energy efficient measures to the building envelope of a multi-apartment building in Sweden: analysis of final energy savings and cost effectiveness2023Ingår i: Buildings, E-ISSN 2075-5309, Vol. 13, nr 10, artikel-id 2654Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    A major challenge in building energy renovation is to cost effectively achieve notable energy savings. This paper investigates cost-effective passive energy-efficiency measures for thermal envelope retrofit of a typical Swedish multi-apartment building from the 1970s. Here, the use of different types of insulation materials for the retrofits of roof, exterior walls, and ground floor are analyzed along with changing windows and doors with varying thermal transmittance values. The cost-effectiveness analysis is based on the net present value of the investment costs of the energy-efficiently measures and the achieved energy cost saving. Different economic scenarios and renovation cases are considered in techno-economic analyses to determine the cost-effective energy-efficiency retrofit measures. The results indicate that improved windows reduce energy demand for space heating by up to 23% and yield the highest final energy savings. However, additional mineral wool roof insulation is the most cost-effective measure under all economic scenarios. This measure gave the lowest ratio of cost effectiveness of about 0.1, which was obtained under the stable scenario. The final energy savings that can be achieved in a cost-effective manner vary between 28% and 61%, depending on the economic scenario and renovation case. This analysis emphasizes the influence of different renovation cases and economic parameters on the cost effectiveness of passive energy-efficiency measures.

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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.
    Lu, Weizhuo
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    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 Sweden2022Ingår i: Buildings, E-ISSN 2075-5309, Vol. 12, nr 8, artikel-id 1244Artikel i tidskrift (Refereegranskat)
    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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  • 3.
    Wei, Bo’an
    et al.
    School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin, China.
    Yang, Bin
    School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin, China.
    Zhang, Weiling
    School of Energy and Safety Engineering, Tianjin Chengjian University, Tianjin, China.
    Liu, Pengju
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för tillämpad fysik och elektronik.
    Fu, Hanliang
    Laboratory of Neuromanagement in Engineering, Xi’an University of Architecture and Technology, Xi’an 710055, China.
    Lv, Zhihan
    Department of Game Design, Faculty of Arts, Uppsala University, Uppsala, Sweden.
    Wang, Faming
    Department of Biosystems, KU Leuven, Leuven, Belgium.
    Construction site hazard identification and worker adverse reaction monitoring using electroencephalograms: a review2024Ingår i: Buildings, E-ISSN 2075-5309, Vol. 14, nr 1, artikel-id 180Artikel, forskningsöversikt (Refereegranskat)
    Abstract [en]

    The construction process is a dynamic one, and the complexity of the working conditions and the high level of uncertainty make the construction industry the third most dangerous industry after mining and agriculture. And since the construction industry is vital to the development of a country, safety during construction is of particular importance. A great deal of research, studies and practices have been conducted to reduce potential risks and improve worker efficiency during the construction process. In recent years, with the rapid development of cognitive neuroscience and the integration of medical technology, various wearable monitoring devices have been widely used in the field of building construction for real-time monitoring of workers’ physical and mental conditions. Among them, the application of EEG (electroencephalogram) in the building construction process enables researchers to gain insight into the physical and mental state of construction workers while performing construction tasks. This paper introduces EEG technology and portable EEG monitoring equipment and summarizes its application in monitoring workers’ adverse reactions (emotion, fatigue, psychological burden, and vigilance) and construction hazard identification during the process of construction in recent years, which provides future EEG research in the field of building construction and construction site safety management.

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