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A machine learning-based surrogate model to approximate optimal building retrofit solutions

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  • Thrampoulidis, Emmanouil
  • Mavromatidis, Georgios
  • Lucchi, Aurelien
  • Orehounig, Kristina

Abstract

The building sector has the highest share of operational energy consumption and greenhouse gas emissions among all sectors. Environmental targets set by many countries impose the need to improve the environmental footprint of the existing building stock. Building retrofit is considered one of the most promising solutions towards this direction. In this paper, a surrogate model for evaluating the necessary building envelope and energy system measures for building retrofit is presented. Artificial neural networks are exploited to build up this model in order to provide a good balance between accuracy and computational cost. The proposed model is trained and tested for the case study of the city of Zurich, in Switzerland, and is compared with one of the most advanced models for building retrofit that uses building simulation and optimization tools. The surrogate model operates on a smaller input set and the time required to derive retrofit solutions is reduced from 3.5 min to 16.4 μsec. Results show that the proposed model can provide significantly reduced computational cost without compromising accuracy for most of the retrofit dimensions. For instance, the retrofit costs and the energy system selections are approximated with an average accuracy of R2=0.9408 and f1score=0.9450, respectively. Finally, yet importantly, such surrogate retrofit models may effectively be used for bottom-up retrofit analyses for wide areas and can contribute towards accelerating the adoption of retrofit measures.

Suggested Citation

  • Thrampoulidis, Emmanouil & Mavromatidis, Georgios & Lucchi, Aurelien & Orehounig, Kristina, 2021. "A machine learning-based surrogate model to approximate optimal building retrofit solutions," Applied Energy, Elsevier, vol. 281(C).
    • Handle: RePEc:eee:appene:v:281:y:2021:i:c:s0306261920314665
    DOI: 10.1016/j.apenergy.2020.116024
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    1. Younhee Choi & Doosam Song & Sungmin Yoon & Junemo Koo, 2021. "Comparison of Factorial and Latin Hypercube Sampling Designs for Meta-Models of Building Heating and Cooling Loads," Energies, MDPI, vol. 14(2), pages 1-23, January.
    2. Ma, Dingyuan & Li, Xiaodong & Lin, Borong & Zhu, Yimin, 2023. "An intelligent retrofit decision-making model for building program planning considering tacit knowledge and multiple objectives," Energy, Elsevier, vol. 263(PB).
    3. Mostafa M. Saad & Ramanunni Parakkal Menon & Ursula Eicker, 2023. "Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis," Energies, MDPI, vol. 16(16), pages 1-28, August.
    4. Aliyon, Kasra & Rajaee, Fatemeh & Ritvanen, Jouni, 2023. "Use of artificial intelligence in reducing energy costs of a post-combustion carbon capture plant," Energy, Elsevier, vol. 278(PA).
    5. Simon Wenninger & Christian Wiethe, 2021. "Benchmarking Energy Quantification Methods to Predict Heating Energy Performance of Residential Buildings in Germany," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 63(3), pages 223-242, June.
    6. Leonidas Zouloumis & Georgios Stergianakos & Nikolaos Ploskas & Giorgos Panaras, 2021. "Dynamic Simulation-Based Surrogate Model for the Dimensioning of Building Energy Systems," Energies, MDPI, vol. 14(21), pages 1-13, November.
    7. Thrampoulidis, Emmanouil & Hug, Gabriela & Orehounig, Kristina, 2023. "Approximating optimal building retrofit solutions for large-scale retrofit analysis," Applied Energy, Elsevier, vol. 333(C).

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