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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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    1. Carstens, Herman & Xia, Xiaohua & Ye, Xianming, 2014. "Improvements to longitudinal Clean Development Mechanism sampling designs for lighting retrofit projects," Applied Energy, Elsevier, vol. 126(C), pages 256-265.
    2. Ye, Xianming & Xia, Xiaohua & Zhang, Jiangfeng, 2013. "Optimal sampling plan for clean development mechanism energy efficiency lighting projects," Applied Energy, Elsevier, vol. 112(C), pages 1006-1015.
    3. Tong, Zheming & Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard B., 2016. "Energy saving potential of natural ventilation in China: The impact of ambient air pollution," Applied Energy, Elsevier, vol. 179(C), pages 660-668.
    4. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2016. "Defining the Influence Region in neighborhood-scale CFD simulations for natural ventilation design," Applied Energy, Elsevier, vol. 182(C), pages 625-633.
    5. Chen, Yujiao & Malkawi, Ali & Liu, Zhu & Freeman, Richard Barry & Tong, Zheming, 2016. "Energy Saving Potential of Natural Ventilation in China: The Impact of Ambient Air Pollution," Scholarly Articles 27733689, Harvard University Department of Economics.
    6. Huang, Yu & Niu, Jian-lei & Chung, Tse-ming, 2013. "Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities," Applied Energy, Elsevier, vol. 103(C), pages 97-108.
    7. Chua, K.J. & Chou, S.K. & Yang, W.M. & Yan, J., 2013. "Achieving better energy-efficient air conditioning – A review of technologies and strategies," Applied Energy, Elsevier, vol. 104(C), pages 87-104.
    8. Xia, Xiaohua & Zhang, Jiangfeng, 2013. "Mathematical description for the measurement and verification of energy efficiency improvement," Applied Energy, Elsevier, vol. 111(C), pages 247-256.
    9. Tronchin, Lamberto & Fabbri, Kristian, 2012. "Energy Performance Certificate of building and confidence interval in assessment: An Italian case study," Energy Policy, Elsevier, vol. 48(C), pages 176-184.
    10. Tong, Zheming & Chen, Yujiao & Malkawi, Ali, 2017. "Estimating natural ventilation potential for high-rise buildings considering boundary layer meteorology," Applied Energy, Elsevier, vol. 193(C), pages 276-286.
    11. Kang, Lixia & Liu, Yongzhong, 2015. "Multi-objective optimization on a heat exchanger network retrofit with a heat pump and analysis of CO2 emissions control," Applied Energy, Elsevier, vol. 154(C), pages 696-708.
    12. Qian, Dong & Guo, Ju’e, 2014. "Research on the energy-saving and revenue sharing strategy of ESCOs under the uncertainty of the value of Energy Performance Contracting Projects," Energy Policy, Elsevier, vol. 73(C), pages 710-721.
    13. Ye, Xianming & Xia, Xiaohua, 2016. "Optimal metering plan for measurement and verification on a lighting case study," Energy, Elsevier, vol. 95(C), pages 580-592.
    14. Carstens, Herman & Xia, Xiaohua & Yadavalli, Sarma, 2018. "Measurement uncertainty in energy monitoring: Present state of the art," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 2791-2805.
    15. Fan, Yuling & Xia, Xiaohua, 2017. "A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance," Applied Energy, Elsevier, vol. 189(C), pages 327-335.
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    Cited by:

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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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