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In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures

Author

Listed:
  • Iole Nardi

    (ENEA Casaccia Research Center, 00123 Rome, Italy)

  • Elena Lucchi

    (Department of Architecture, Built Environment and Construction Engineering (DABC), Politecnico di Milano, 20133 Milan, Italy)

Abstract

Different standard methods for the assessment of the thermal performance of the building envelope are used: analogy with coeval building, theoretical method, heat flow meter measurement, simple hot box, infrared thermography, and thermometric method. Review papers on these methods, applied in situ and in laboratory, have been published, focusing on theory, equipment, metrological performance, test conditions and data acquisition, data analysis, benefits, and limitations. However, steps forward have been done and not been deepened in previous works: in fact, the representative points method and the weighted area method have been proposed, too, whilst artificial intelligence and data-driven methods have begun to prove the reliability also in the U-value prevision using available datasets. Considering this context, this work aims at updating the literature background considering exclusively in situ methods. The work starts from bibliometric and scientometric analysis not previously conducted: this helped to group the methods and to sketch the innovations and the future perspectives. Indeed, from the bibliometric and scientometric literature analysis what emerged was (i) the richness of the background on this topic, especially in the recent years, (ii) two macro-groups (methods with and without measurements), and (iii) the importance of paper keywords (otherwise, interesting papers are eluded by the output of simple database queries). The method study that followed aims at providing (i) a broader view of the thermal transmittance (U-value) assessment procedures, including the utmost recent applications, proposal, and outlooks in this field, (ii) the understanding on the fundamental theories of the techniques, (iii) practical advice for building-envelope assessment, focusing on the advantages and limitations useful for professionals and researchers involved in the energy audit, conservation, or refurbishment of building stock, (iv) the identification of the interconnection between the techniques that often rely on one another, and (v) final remarks and future perspective of the procedures, which embrace the use of artificial intelligence (AI). From the topic analysis, as a result, it emerged that this is an open field for future research, especially with the implementation of AI, which requires good datasets and trials on the models’ architectures, in terms of input layer, number of hidden layer and neurons, and percentage of data to be employed for model training and testing.

Suggested Citation

  • Iole Nardi & Elena Lucchi, 2023. "In Situ Thermal Transmittance Assessment of the Building Envelope: Practical Advice and Outlooks for Standard and Innovative Procedures," Energies, MDPI, vol. 16(8), pages 1-31, April.
  • Handle: RePEc:gam:jeners:v:16:y:2023:i:8:p:3319-:d:1118676
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    References listed on IDEAS

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    1. Mirco Andreotti & Marta Calzolari & Pietromaria Davoli & Luisa Dias Pereira & Elena Lucchi & Roberto Malaguti, 2020. "Design and Construction of a New Metering Hot Box for the In Situ Hygrothermal Measurement in Dynamic Conditions of Historic Masonries," Energies, MDPI, vol. 13(11), pages 1-21, June.
    2. Ascione, Fabrizio & Ceroni, Francesca & De Masi, Rosa Francesca & de’ Rossi, Filippo & Pecce, Maria Rosaria, 2017. "Historical buildings: Multidisciplinary approach to structural/energy diagnosis and performance assessment," Applied Energy, Elsevier, vol. 185(P2), pages 1517-1528.
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    4. Albatici, Rossano & Tonelli, Arnaldo M. & Chiogna, Michela, 2015. "A comprehensive experimental approach for the validation of quantitative infrared thermography in the evaluation of building thermal transmittance," Applied Energy, Elsevier, vol. 141(C), pages 218-228.
    5. Domenico Palladino & Iole Nardi & Cinzia Buratti, 2020. "Artificial Neural Network for the Thermal Comfort Index Prediction: Development of a New Simplified Algorithm," Energies, MDPI, vol. 13(17), pages 1-27, September.
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    7. Seyoung Park & Seo Hoon Kim & Hakgeun Jeong & Sung Lok Do & Jonghun Kim, 2021. "In Situ Evaluation of the U-Value of a Window Using the Infrared Method," Energies, MDPI, vol. 14(7), pages 1-14, March.
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    10. Seo-Hoon Kim & Jung-Hun Lee & Jong-Hun Kim & Seung-Hwan Yoo & Hak-Geun Jeong, 2018. "The Feasibility of Improving the Accuracy of In Situ Measurements in the Air-Surface Temperature Ratio Method," Energies, MDPI, vol. 11(7), pages 1-18, July.
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    12. Asdrubali, Francesco & Baldinelli, Giorgio & Bianchi, Francesco & Costarelli, Danilo & Rotili, Antonella & Seracini, Marco & Vinti, Gianluca, 2018. "Detection of thermal bridges from thermographic images by means of image processing approximation algorithms," Applied Mathematics and Computation, Elsevier, vol. 317(C), pages 160-171.
    13. Bienvenido-Huertas, David & Moyano, Juan & Rodríguez-Jiménez, Carlos E. & Marín, David, 2019. "Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method," Applied Energy, Elsevier, vol. 233, pages 1-14.
    14. Seo-Hoon Kim & Jong-Hun Kim & Hak-Geun Jeong & Kyoo-Dong Song, 2018. "Reliability Field Test of the Air–Surface Temperature Ratio Method for In Situ Measurement of U-Values," Energies, MDPI, vol. 11(4), pages 1-15, March.
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    1. Eva Schito & Elena Lucchi, 2023. "Advances in the Optimization of Energy Use in Buildings," Sustainability, MDPI, vol. 15(18), pages 1-3, September.
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