IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v233-234y2019ip1-14.html
   My bibliography  Save this article

Applying an artificial neural network to assess thermal transmittance in walls by means of the thermometric method

Author

Listed:
  • Bienvenido-Huertas, David
  • Moyano, Juan
  • Rodríguez-Jiménez, Carlos E.
  • Marín, David

Abstract

Most of the existing building stock has a deficient energy behaviour. The thermal transmittance of façades is among those aspects which most affect this situation. In this paper, the calculation procedure with correction for storage effects from ISO 9869-1 was applied to the thermometric method to determine the U-value. Due to the need for determining the number and type of layers that compose the wall to apply the calculation, a multilayer perceptron has been developed to estimate the U-value. From the different model configurations suggested, the most adequate architecture was the one with 14 nodes in the hidden layer without making transformations in the input variables. Valid results have been obtained by the multilayer perceptron for the case studies analysed from different building periods, with deviations lower than 20% between the measured value and the expected one, varying the test duration according to the thermal resistance of the wall and the temperature variations. Furthermore, it is not necessary to carry out a data post-processing for the model, so this fact simplifies and hastens the calculation procedure.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:233-234:y:2019:i::p:1-14
    DOI: 10.1016/j.apenergy.2018.10.052
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261918316118
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2018.10.052?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Todorka Samardzioska & Roberta Apostolska, 2016. "Measurement of Heat-Flux of New Type Façade Walls," Sustainability, MDPI, vol. 8(10), pages 1-11, October.
    2. Reilly, Aidan & Kinnane, Oliver, 2017. "The impact of thermal mass on building energy consumption," Applied Energy, Elsevier, vol. 198(C), pages 108-121.
    3. Corgnati, Stefano Paolo & Fabrizio, Enrico & Filippi, Marco & Monetti, Valentina, 2013. "Reference buildings for cost optimal analysis: Method of definition and application," Applied Energy, Elsevier, vol. 102(C), pages 983-993.
    4. Buratti, Cinzia & Barelli, Linda & Moretti, Elisa, 2012. "Application of artificial neural network to predict thermal transmittance of wooden windows," Applied Energy, Elsevier, vol. 98(C), pages 425-432.
    5. 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.
    6. Fokaides, Paris A. & Kalogirou, Soteris A., 2011. "Application of infrared thermography for the determination of the overall heat transfer coefficient (U-Value) in building envelopes," Applied Energy, Elsevier, vol. 88(12), pages 4358-4365.
    7. Kalogirou, Soteris A., 2000. "Applications of artificial neural-networks for energy systems," Applied Energy, Elsevier, vol. 67(1-2), pages 17-35, September.
    8. Pino-Mejías, Rafael & Pérez-Fargallo, Alexis & Rubio-Bellido, Carlos & Pulido-Arcas, Jesús A., 2017. "Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO2 emissions," Energy, Elsevier, vol. 118(C), pages 24-36.
    9. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    10. David Bienvenido-Huertas & Roberto Rodríguez-Álvaro & Juan José Moyano & Fernando Rico & David Marín, 2018. "Determining the U -Value of Façades Using the Thermometric Method: Potentials and Limitations," Energies, MDPI, vol. 11(2), pages 1-17, February.
    11. Ohlsson, K.E.A. & Olofsson, T., 2014. "Quantitative infrared thermography imaging of the density of heat flow rate through a building element surface," Applied Energy, Elsevier, vol. 134(C), pages 499-505.
    12. Doo Sung Choi & Myeong Jin Ko, 2017. "Comparison of Various Analysis Methods Based on Heat Flowmeters and Infrared Thermography Measurements for the Evaluation of the In Situ Thermal Transmittance of Opaque Exterior Walls," Energies, MDPI, vol. 10(7), pages 1-22, July.
    13. Giuliano Dall'O' & Luca Sarto & Angela Panza, 2013. "Infrared Screening of Residential Buildings for Energy Audit Purposes: Results of a Field Test," Energies, MDPI, vol. 6(8), pages 1-20, July.
    14. Kialashaki, Arash & Reisel, John R., 2013. "Modeling of the energy demand of the residential sector in the United States using regression models and artificial neural networks," Applied Energy, Elsevier, vol. 108(C), pages 271-280.
    15. 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.
    16. 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.
    17. Singh, Manoj Kumar & Mahapatra, Sadhan & Teller, Jacques, 2013. "An analysis on energy efficiency initiatives in the building stock of Liege, Belgium," Energy Policy, Elsevier, vol. 62(C), pages 729-741.
    18. Al-Sanea, Sami A. & Zedan, M.F., 2011. "Improving thermal performance of building walls by optimizing insulation layer distribution and thickness for same thermal mass," Applied Energy, Elsevier, vol. 88(9), pages 3113-3124.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Astrid Tijskens & Hans Janssen & Staf Roels, 2019. "Optimising Convolutional Neural Networks to Predict the Hygrothermal Performance of Building Components," Energies, MDPI, vol. 12(20), pages 1-18, October.
    2. Víctor Echarri-Iribarren & Cristina Sotos-Solano & Almudena Espinosa-Fernández & Raúl Prado-Govea, 2019. "The Passivhaus Standard in the Spanish Mediterranean: Evaluation of a House’s Thermal Behaviour of Enclosures and Airtightness," Sustainability, MDPI, vol. 11(13), pages 1-25, July.
    3. Sanjin Gumbarević & Bojan Milovanović & Bojana Dalbelo Bašić & Mergim Gaši, 2022. "Combining Deep Learning and the Heat Flux Method for In-Situ Thermal-Transmittance Measurement Improvement," Energies, MDPI, vol. 15(14), pages 1-19, July.
    4. Lee, Juyong & Cho, Youngsang, 2022. "National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?," Energy, Elsevier, vol. 239(PD).
    5. 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.
    6. David Bienvenido-Huertas & Carlos Rubio-Bellido & Juan Luis Pérez-Ordóñez & Fernando Martínez-Abella, 2019. "Estimating Adaptive Setpoint Temperatures Using Weather Stations," Energies, MDPI, vol. 12(7), pages 1-47, March.
    7. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    8. Daniel Sánchez-García & David Bienvenido-Huertas & Mónica Tristancho-Carvajal & Carlos Rubio-Bellido, 2019. "Adaptive Comfort Control Implemented Model (ACCIM) for Energy Consumption Predictions in Dwellings under Current and Future Climate Conditions: A Case Study Located in Spain," Energies, MDPI, vol. 12(8), pages 1-22, April.
    9. Chukwuemeka Uguba Owora & Samson Kolawole Fasogbon, 2020. "Rainfall Variability and Trends over Central Ethiopia," International Journal of Environmental Sciences & Natural Resources, Juniper Publishers Inc., vol. 24(4), pages 145-156, May.
    10. David Bienvenido-Huertas & Jesús A. Pulido-Arcas & Carlos Rubio-Bellido & Alexis Pérez-Fargallo, 2021. "Prediction of Fuel Poverty Potential Risk Index Using Six Regression Algorithms: A Case-Study of Chilean Social Dwellings," Sustainability, MDPI, vol. 13(5), pages 1-30, February.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Bienvenido-Huertas, David & Moyano, Juan & Marín, David & Fresco-Contreras, Rafael, 2019. "Review of in situ methods for assessing the thermal transmittance of walls," Renewable and Sustainable Energy Reviews, Elsevier, vol. 102(C), pages 356-371.
    2. 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.
    3. David Bienvenido-Huertas, 2020. "Assessing the Environmental Impact of Thermal Transmittance Tests Performed in Façades of Existing Buildings: The Case of Spain," Sustainability, MDPI, vol. 12(15), pages 1-18, August.
    4. Martin, Miguel & Chong, Adrian & Biljecki, Filip & Miller, Clayton, 2022. "Infrared thermography in the built environment: A multi-scale review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
    5. 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.
    6. Doo Sung Choi & Myeong Jin Ko, 2019. "Analysis of Convergence Characteristics of Average Method Regulated by ISO 9869-1 for Evaluating In Situ Thermal Resistance and Thermal Transmittance of Opaque Exterior Walls," Energies, MDPI, vol. 12(10), pages 1-18, May.
    7. 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.
    8. Lucchi, Elena, 2018. "Applications of the infrared thermography in the energy audit of buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3077-3090.
    9. Baldinelli, Giorgio & Bianchi, Francesco & Rotili, Antonella & Costarelli, Danilo & Seracini, Marco & Vinti, Gianluca & Asdrubali, Francesco & Evangelisti, Luca, 2018. "A model for the improvement of thermal bridges quantitative assessment by infrared thermography," Applied Energy, Elsevier, vol. 211(C), pages 854-864.
    10. Doo Sung Choi & Myeong Jin Ko, 2017. "Comparison of Various Analysis Methods Based on Heat Flowmeters and Infrared Thermography Measurements for the Evaluation of the In Situ Thermal Transmittance of Opaque Exterior Walls," Energies, MDPI, vol. 10(7), pages 1-22, July.
    11. Rasooli, Arash & Itard, Laure, 2019. "In-situ rapid determination of walls’ thermal conductivity, volumetric heat capacity, and thermal resistance, using response factors," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    12. O'Grady, Małgorzata & Lechowska, Agnieszka A. & Harte, Annette M., 2017. "Quantification of heat losses through building envelope thermal bridges influenced by wind velocity using the outdoor infrared thermography technique," Applied Energy, Elsevier, vol. 208(C), pages 1038-1052.
    13. Hye-Ryeong Nam & Seo-Hoon Kim & Seol-Yee Han & Sung-Jin Lee & Won-Hwa Hong & Jong-Hun Kim, 2020. "Statistical Methodology for the Definition of Standard Model for Energy Analysis of Residential Buildings in Korea," Energies, MDPI, vol. 13(21), pages 1-16, November.
    14. Marianna Rotilio & Federica Cucchiella & Pierluigi De Berardinis & Vincenzo Stornelli, 2018. "Thermal Transmittance Measurements of the Historical Masonries: Some Case Studies," Energies, MDPI, vol. 11(11), pages 1-18, November.
    15. David Bienvenido-Huertas & Roberto Rodríguez-Álvaro & Juan José Moyano & Fernando Rico & David Marín, 2018. "Determining the U -Value of Façades Using the Thermometric Method: Potentials and Limitations," Energies, MDPI, vol. 11(2), pages 1-17, February.
    16. Luca Evangelisti & Leone Barbaro & Claudia Guattari & Edoardo De Cristo & Roberto De Lieto Vollaro & Francesco Asdrubali, 2024. "Comparison between Direct and Indirect Heat Flux Measurement Techniques: Preliminary Laboratory Tests," Energies, MDPI, vol. 17(12), pages 1-16, June.
    17. Brandão de Vasconcelos, Ana & Pinheiro, Manuel Duarte & Manso, Armando & Cabaço, António, 2015. "A Portuguese approach to define reference buildings for cost-optimal methodologies," Applied Energy, Elsevier, vol. 140(C), pages 316-328.
    18. Blanca Tejedor & Eva Barreira & Vasco Peixoto de Freitas & Tomasz Kisilewicz & Katarzyna Nowak-Dzieszko & Umberto Berardi, 2020. "Impact of Stationary and Dynamic Conditions on the U-Value Measurements of Heavy-Multi Leaf Walls by Quantitative IRT," Energies, MDPI, vol. 13(24), pages 1-19, December.
    19. Buratti, C. & Barbanera, M. & Palladino, D., 2014. "An original tool for checking energy performance and certification of buildings by means of Artificial Neural Networks," Applied Energy, Elsevier, vol. 120(C), pages 125-132.
    20. Aurora Greta Ruggeri & Laura Gabrielli & Massimiliano Scarpa, 2020. "Energy Retrofit in European Building Portfolios: A Review of Five Key Aspects," Sustainability, MDPI, vol. 12(18), pages 1-38, September.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:233-234:y:2019:i::p:1-14. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.