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A machine learning-based framework for cost-optimal building retrofit

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  • Deb, Chirag
  • Dai, Zhonghao
  • Schlueter, Arno

Abstract

The current process of analysing building retrofit strategies relies on physics-based, thermal balance models. However, these models are oblivious to the significance of the input variables for devising the retrofit strategies. This leads to the process of exhaustive search for obtaining the cost-optimal retrofit strategy. On the contrary, this work presents a framework for a data-driven, cost-optimal retrofit analysis based on machine learning (ML) techniques which capitalizes on the importance of the input variables. The framework involves four steps, which are feature selection, model development, feature significance and cost-optimal retrofit analysis.

Suggested Citation

  • Deb, Chirag & Dai, Zhonghao & Schlueter, Arno, 2021. "A machine learning-based framework for cost-optimal building retrofit," Applied Energy, Elsevier, vol. 294(C).
    • Handle: RePEc:eee:appene:v:294:y:2021:i:c:s030626192100458x
    DOI: 10.1016/j.apenergy.2021.116990
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    References listed on IDEAS

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    Citations

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    Cited by:

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    2. Kyoungcheol Oh & Eui-Jong Kim & Chang-Young Park, 2022. "A Physical Model-Based Data-Driven Approach to Overcome Data Scarcity and Predict Building Energy Consumption," Sustainability, MDPI, vol. 14(15), pages 1-14, August.
    3. 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).
    4. Lee, Seonho & Kim, Jiwon & Byun, Jaewon & Joo, Junghee & Lee, Yoonjae & Kim, Taehyun & Hwangbo, Soonho & Han, Jeehoon & Kim, Sung-Kon & Lee, Jechan, 2023. "Environmentally-viable utilization of chicken litter as energy recovery and electrode production: A machine learning approach," Applied Energy, Elsevier, vol. 350(C).
    5. Venkatraj, V. & Dixit, M.K., 2022. "Challenges in implementing data-driven approaches for building life cycle energy assessment: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
    6. Mayer, Kevin & Haas, Lukas & Huang, Tianyuan & Bernabé-Moreno, Juan & Rajagopal, Ram & Fischer, Martin, 2023. "Estimating building energy efficiency from street view imagery, aerial imagery, and land surface temperature data," Applied Energy, Elsevier, vol. 333(C).
    7. Wiethe, Christian & Wenninger, Simon, 2023. "The influence of building energy performance prediction accuracy on retrofit rates," Energy Policy, Elsevier, vol. 177(C).
    8. Hamidreza Seraj & Ali Bahadori-Jahromi & Shiva Amirkhani, 2024. "Developing a Data-Driven AI Model to Enhance Energy Efficiency in UK Residential Buildings," Sustainability, MDPI, vol. 16(8), pages 1-16, April.

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