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Building retrofit optimization models using notch test data considering energy performance certificate compliance

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  • Fan, Yuling
  • Xia, Xiaohua

Abstract

Determining a systematic whole-building retrofit plan for envelope components and indoor appliances to achieve targets such as cost savings and policy compliance is a challenging task. To be specific, the systematic whole-building retrofit problem, when solved by an optimization approach, is highly complicated. It is sometimes even impossible to find a solution with given computation resources and algorithms. In addition, a costly comprehensive bottom-up audit is required to establish the parameters of the problem. This study presents two models to reduce the complexity of the systematic whole-building retrofit optimization problem. Firstly, the proposed models use the grouping concept to optimize the retrofit of subsystems in a building instead of individual components/appliances, which reduces the dimension of the problem effectively. Secondly, the models use so-called ‘notch test’ data, which are sampled and verified savings of an intervention, to eliminate the need for bottom-up energy audits. This further simplifies the retrofit optimization problem and reduces the retrofit cost. The models are based on our previous work and aim at energy savings maximization and payback period minimization, considering the green building policy and tax incentive initiatives. A case study shows that about 2530 MWh energy savings and an A rating from the energy performance certificate standard can be obtained with a payback period of 59 months, which verifies the feasibility and effectiveness of the models proposed.

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  • Fan, Yuling & Xia, Xiaohua, 2018. "Building retrofit optimization models using notch test data considering energy performance certificate compliance," Applied Energy, Elsevier, vol. 228(C), pages 2140-2152.
    • Handle: RePEc:eee:appene:v:228:y:2018:i:c:p:2140-2152
    DOI: 10.1016/j.apenergy.2018.07.043
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    References listed on IDEAS

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    2. Zhu, Chuanqi & Tian, Wei & Yin, Baoquan & Li, Zhanyong & Shi, Jiaxin, 2020. "Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms," Applied Energy, Elsevier, vol. 268(C).
    3. Martin, Rit & Arthur, Thomas & Jonathan, Villot & Mathieu, Thorel & Enora, Garreau & Robin, Girard, 2024. "SHAPE: A temporal optimization model for residential buildings retrofit to discuss policy objectives," Applied Energy, Elsevier, vol. 361(C).
    4. Shadram, Farshid & Bhattacharjee, Shimantika & Lidelöw, Sofia & Mukkavaara, Jani & Olofsson, Thomas, 2020. "Exploring the trade-off in life cycle energy of building retrofit through optimization," Applied Energy, Elsevier, vol. 269(C).
    5. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).

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