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A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance

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  • Fan, Yuling
  • Xia, Xiaohua

Abstract

Retrofitting existing buildings with energy-efficient facilities is an effective method to improve their energy efficiency, especially for old buildings. A multi-objective optimization model for building envelope retrofitting is presented. Envelope components including windows, external walls and roofs are considered to be retrofitted. Installation of a rooftop solar panel system is also taken into consideration in this study. Rooftop solar panels are modeled with their degradation and a maintenance scheme is studied for sustainability of energy and its long-term effect on the retrofitting plan. The purpose is to make the best use of financial investment to maximize energy savings and economic benefits. In particular, net present value, the payback period and energy savings are taken as the main performance indicators of the retrofitting plan. The multi-objective optimization problem is formulated as a non-linear integer programming problem and solved by a weighted sum method. Results of applying the designed retrofitting plan to a 50-year-old building consisting of 66 apartments demonstrated the effectiveness of the proposed model.

Suggested Citation

  • 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.
  • Handle: RePEc:eee:appene:v:189:y:2017:i:c:p:327-335
    DOI: 10.1016/j.apenergy.2016.12.077
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    References listed on IDEAS

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    1. Méndez Echenagucia, Tomás & Capozzoli, Alfonso & Cascone, Ylenia & Sassone, Mario, 2015. "The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis," Applied Energy, Elsevier, vol. 154(C), pages 577-591.
    2. Zhou, Zhihua & Feng, Lei & Zhang, Shuzhen & Wang, Chendong & Chen, Guanyi & Du, Tao & Li, Yasong & Zuo, Jian, 2016. "The operational performance of “net zero energy building”: A study in China," Applied Energy, Elsevier, vol. 177(C), pages 716-728.
    3. Martinot, E. & Cabraal, A. & Mathur, S., 2001. "World Bank/GEF solar home system projects: experiences and lessons learned 1993-2000," Renewable and Sustainable Energy Reviews, Elsevier, vol. 5(1), pages 39-57, March.
    4. 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.
    5. Zhang, Tiantian & Tan, Yufei & Yang, Hongxing & Zhang, Xuedan, 2016. "The application of air layers in building envelopes: A review," Applied Energy, Elsevier, vol. 165(C), pages 707-734.
    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. Nair, Gireesh & Gustavsson, Leif & Mahapatra, Krushna, 2010. "Owners perception on the adoption of building envelope energy efficiency measures in Swedish detached houses," Applied Energy, Elsevier, vol. 87(7), pages 2411-2419, July.
    8. Wu, Zhou & Wang, Bo & Xia, Xiaohua, 2016. "Large-scale building energy efficiency retrofit: Concept, model and control," Energy, Elsevier, vol. 109(C), pages 456-465.
    9. Koo, Choongwan & Park, Sungki & Hong, Taehoon & Park, Hyo Seon, 2014. "An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method," Applied Energy, Elsevier, vol. 115(C), pages 205-215.
    10. Khatib, Hisham, 2012. "IEA World Energy Outlook 2011—A comment," Energy Policy, Elsevier, vol. 48(C), pages 737-743.
    11. Wu, Zhou & Tazvinga, Henerica & Xia, Xiaohua, 2015. "Demand side management of photovoltaic-battery hybrid system," Applied Energy, Elsevier, vol. 148(C), pages 294-304.
    12. Bastani, Arash & Haghighat, Fariborz & Kozinski, Janusz, 2014. "Designing building envelope with PCM wallboards: Design tool development," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 554-562.
    13. Mavromatidis, Lazaros Elias & Bykalyuk, Anna & Lequay, Hervé, 2013. "Development of polynomial regression models for composite dynamic envelopes’ thermal performance forecasting," Applied Energy, Elsevier, vol. 104(C), pages 379-391.
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