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A Procedure for Automating Energy Analyses in the BIM Context Exploiting Artificial Neural Networks and Transfer Learning Technique

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  • Mikhail Demianenko

    (Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy)

  • Carlo Iapige De Gaetani

    (Department of Civil and Environmental Engineering, Politecnico di Milano, 20133 Milan, Italy)

Abstract

One of the main benefits of Building Information Modelling is the capability of improving the decision-making process thanks performing what-if tests on digital twins of the building to be realized. Pairing BIM models to Building Energy Models allows designers to determine in advance the energy consumption of the building, improving sustainability of the construction. The challenge is to consider as many elements involved in the energy balance as possible and shuffling their parameters within a certain range. In this work, the automatic creation of a relevant set of design options to be analyzed for searching the optimum has been carried out. Firstly, the usual workflow that would be applied manually has been automatically followed by running scripts and codes, depending just on the initial setup given by the user. Although the procedure is very resource consuming, the main advancement relies in the reduction of the manual intervention and the possibility of creating large datasets of design options, avoiding gross errors. Secondly, Artificial Neural Networks and Transfer Learning techniques are applied to speed up the process of dataset creation. With such approach, the same dataset has been created, with about 30% of initial data and without significant loss of accuracy.

Suggested Citation

  • Mikhail Demianenko & Carlo Iapige De Gaetani, 2021. "A Procedure for Automating Energy Analyses in the BIM Context Exploiting Artificial Neural Networks and Transfer Learning Technique," Energies, MDPI, vol. 14(10), pages 1-18, May.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:10:p:2956-:d:558436
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    References listed on IDEAS

    as
    1. Carlo Iapige De Gaetani & Andrea Macchi & Pasquale Perri, 2020. "Joint Analysis of Cost and Energy Savings for Preliminary Design Alternative Assessment," Sustainability, MDPI, vol. 12(18), pages 1-18, September.
    2. Kamel, Ehsan & Memari, Ali M., 2018. "Automated Building Energy Modeling and Assessment Tool (ABEMAT)," Energy, Elsevier, vol. 147(C), pages 15-24.
    3. Cristina Piselli & Jessica Romanelli & Matteo Di Grazia & Augusto Gavagni & Elisa Moretti & Andrea Nicolini & Franco Cotana & Francesco Strangis & Henk J. L. Witte & Anna Laura Pisello, 2020. "An Integrated HBIM Simulation Approach for Energy Retrofit of Historical Buildings Implemented in a Case Study of a Medieval Fortress in Italy," Energies, MDPI, vol. 13(10), pages 1-21, May.
    4. Guofeng Ma & Ying Liu & Shanshan Shang, 2019. "A Building Information Model (BIM) and Artificial Neural Network (ANN) Based System for Personal Thermal Comfort Evaluation and Energy Efficient Design of Interior Space," Sustainability, MDPI, vol. 11(18), pages 1-26, September.
    5. Jitka Mohelníková & Miloslav Novotný & Pavla Mocová, 2020. "Evaluation of School Building Energy Performance and Classroom Indoor Environment," Energies, MDPI, vol. 13(10), pages 1-17, May.
    6. Mohammad K. Najjar & Vivian W. Y. Tam & Leandro Torres Di Gregorio & Ana Catarina Jorge Evangelista & Ahmed W. A. Hammad & Assed Haddad, 2019. "Integrating Parametric Analysis with Building Information Modeling to Improve Energy Performance of Construction Projects," Energies, MDPI, vol. 12(8), pages 1-22, April.
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    Cited by:

    1. Coraci, Davide & Brandi, Silvio & Hong, Tianzhen & Capozzoli, Alfonso, 2023. "Online transfer learning strategy for enhancing the scalability and deployment of deep reinforcement learning control in smart buildings," Applied Energy, Elsevier, vol. 333(C).

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