IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v14y2021i16p5188-d619384.html
   My bibliography  Save this article

Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks

Author

Listed:
  • Martín Pensado-Mariño

    (Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain)

  • Lara Febrero-Garrido

    (Defense University Center, Spanish Naval Academy, Plaza de España, s/n, 36920 Marín, Spain)

  • Estibaliz Pérez-Iribarren

    (TENECO Research Group, Department of Mechanical Engineering, University of La Rioja, Calle San Jose de Calasanz, 31, 26004 Logroño, Spain)

  • Pablo Eguía Oller

    (Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain)

  • Enrique Granada-Álvarez

    (Department of Mechanical Engineering, Heat Engines and Fluids Mechanics, Industrial Engineering School, University of Vigo, Maxwell s/n, 36310 Vigo, Spain)

Abstract

Accurate forecasting of a building thermal performance can help to optimize its energy consumption. In addition, obtaining the Heat Loss Coefficient (HLC) allows characterizing the thermal envelope of the building under conditions of use. The aim of this work is to study the thermal inertia of a building developing a new methodology based on Long Short-Term Memory (LSTM) neural networks. This approach was applied to the Rectorate building of the University of Basque Country (UPV/EHU), located in the north of Spain. A comparison of different time-lags selected to catch the thermal inertia has been carried out using the CV(RMSE) and the MBE errors, as advised by ASHRAE. The main contribution of this work lies in the analysis of thermal inertia detection and its influence on the thermal behavior of the building, obtaining a model capable of predicting the thermal demand with an error between 12 and 21%. Moreover, the viability of LSTM neural networks to estimate the HLC of an in-use building with an error below 4% was demonstrated.

Suggested Citation

  • Martín Pensado-Mariño & Lara Febrero-Garrido & Estibaliz Pérez-Iribarren & Pablo Eguía Oller & Enrique Granada-Álvarez, 2021. "Estimation of Heat Loss Coefficient and Thermal Demands of In-Use Building by Capturing Thermal Inertia Using LSTM Neural Networks," Energies, MDPI, vol. 14(16), pages 1-14, August.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:16:p:5188-:d:619384
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/14/16/5188/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/14/16/5188/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Menezes, Anna Carolina & Cripps, Andrew & Bouchlaghem, Dino & Buswell, Richard, 2012. "Predicted vs. actual energy performance of non-domestic buildings: Using post-occupancy evaluation data to reduce the performance gap," Applied Energy, Elsevier, vol. 97(C), pages 355-364.
    2. Muthalagappan Narayanan & Aline Ferreira de Lima & André Felipe Oliveira de Azevedo Dantas & Walter Commerell, 2020. "Development of a Coupled TRNSYS-MATLAB Simulation Framework for Model Predictive Control of Integrated Electrical and Thermal Residential Renewable Energy System," Energies, MDPI, vol. 13(21), pages 1-29, November.
    Full references (including those not matched with items on IDEAS)

    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. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    2. Wang, Lan & Lee, Eric W.M. & Hussian, Syed Asad & Yuen, Anthony Chun Yin & Feng, Wei, 2021. "Quantitative impact analysis of driving factors on annual residential building energy end-use combining machine learning and stochastic methods," Applied Energy, Elsevier, vol. 299(C).
    3. De Boeck, L. & Verbeke, S. & Audenaert, A. & De Mesmaeker, L., 2015. "Improving the energy performance of residential buildings: A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 960-975.
    4. Abdulazeez Rotimi & Ali Bahadori-Jahromi & Anastasia Mylona & Paulina Godfrey & Darren Cook, 2017. "Estimation and Validation of Energy Consumption in UK Existing Hotel Building Using Dynamic Simulation Software," Sustainability, MDPI, vol. 9(8), pages 1-18, August.
    5. Bai, Yefei & Yu, Cong & Pan, Wei, 2024. "Systematic examination of energy performance gap in low-energy buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
    6. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    7. Zulay Giménez & Claudio Mourgues & Luis F. Alarcón & Harrison Mesa & Eugenio Pellicer, 2020. "Value Analysis Model to Support the Building Design Process," Sustainability, MDPI, vol. 12(10), pages 1-24, May.
    8. Naylor, Sophie & Gillott, Mark & Lau, Tom, 2018. "A review of occupant-centric building control strategies to reduce building energy use," Renewable and Sustainable Energy Reviews, Elsevier, vol. 96(C), pages 1-10.
    9. Zhao, Dong-Xue & He, Bao-Jie & Johnson, Christine & Mou, Ben, 2015. "Social problems of green buildings: From the humanistic needs to social acceptance," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 1594-1609.
    10. Ljubomir Jankovic, 2016. "Reducing Simulation Performance Gap in Hemp-Lime Buildings Using Fourier Filtering †," Sustainability, MDPI, vol. 8(9), pages 1-16, August.
    11. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    12. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    13. Krarti, Moncef & Dubey, Kankana & Howarth, Nicholas, 2019. "Energy productivity analysis framework for buildings: a case study of GCC region," Energy, Elsevier, vol. 167(C), pages 1251-1265.
    14. Ardeshir Mahdavi & Christiane Berger & Hadeer Amin & Eleni Ampatzi & Rune Korsholm Andersen & Elie Azar & Verena M. Barthelmes & Matteo Favero & Jakob Hahn & Dolaana Khovalyg & Henrik N. Knudsen & Ale, 2021. "The Role of Occupants in Buildings’ Energy Performance Gap: Myth or Reality?," Sustainability, MDPI, vol. 13(6), pages 1-44, March.
    15. Domenico Curto & Vincenzo Franzitta & Andrea Guercio & Domenico Panno, 2021. "Energy Retrofit. A Case Study—Santi Romano Dormitory on the Palermo University," Sustainability, MDPI, vol. 13(24), pages 1-13, December.
    16. Xing Shi & Binghui Si & Jiangshan Zhao & Zhichao Tian & Chao Wang & Xing Jin & Xin Zhou, 2019. "Magnitude, Causes, and Solutions of the Performance Gap of Buildings: A Review," Sustainability, MDPI, vol. 11(3), pages 1-21, February.
    17. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
    18. Sooyoun Cho & Jeehang Lee & Jumi Baek & Gi-Seok Kim & Seung-Bok Leigh, 2019. "Investigating Primary Factors Affecting Electricity Consumption in Non-Residential Buildings Using a Data-Driven Approach," Energies, MDPI, vol. 12(21), pages 1-23, October.
    19. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
    20. Hamburg, Anti & Kuusk, Kalle & Mikola, Alo & Kalamees, Targo, 2020. "Realisation of energy performance targets of an old apartment building renovated to nZEB," Energy, Elsevier, vol. 194(C).

    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:gam:jeners:v:14:y:2021:i:16:p:5188-:d:619384. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.