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Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis

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  • Li, Hong Xian
  • Li, Yan
  • Jiang, Boya
  • Zhang, Limao
  • Wu, Xianguo
  • Lin, Jingyi

Abstract

Retrofitting building envelopes is regarded as an effective solution that can help commercial and individual investors offset daily power usage. However, it is worthwhile to explore a highly efficient approach to seek optimum retrofitting strategies. A novel hybrid approach that integrates energy simulation, Orthogonal Array Testing (OAT), and Data Envelopment Analysis (DEA) is developed in this research to discover optimal solutions for building retrofit. A commercial high-rise building is chosen as a case study, and five parameters are considered, including the exterior envelope fabric, exterior window type, sunshade type, window-to-wall ratio, and airtightness. The energy consumption is first simulated and verified as a baseline. OAT is then employed to conduct experiments and explore potential solutions to the energy optimisation problem, based on which the most efficient strategy is obtained through DEA benchmarking. The identified optimal solution is able to save an annual operation energy of 7.01 kWh/m2, which is also cost-effective. It is also found that the window type and airtightness are significant factors with regard to the energy performance of building envelope retrofit. The study benefits designers and construction managers in determining the optimal solution of retrofitting building envelope for achieving energy-efficient building operations.

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  • Li, Hong Xian & Li, Yan & Jiang, Boya & Zhang, Limao & Wu, Xianguo & Lin, Jingyi, 2020. "Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis," Renewable Energy, Elsevier, vol. 149(C), pages 1414-1423.
    • Handle: RePEc:eee:renene:v:149:y:2020:i:c:p:1414-1423
    DOI: 10.1016/j.renene.2019.10.143
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    2. Xiaodan Huang & Qingyuan Zhang & Ineko Tanaka, 2021. "Optimization of Architectural Form for Thermal Comfort in Naturally Ventilated Gymnasium at Hot and Humid Climate by Orthogonal Experiment," Energies, MDPI, vol. 14(11), pages 1-18, May.
    3. Guo, Jiwei & Dong, Jiankai & Wang, Hongjue & Wang, Yuan & Zou, Bin & Jiang, Yiqiang, 2022. "Study on the demand response potential of an actively ventilated building: Parametric and scenario analysis," Energy, Elsevier, vol. 238(PC).
    4. Guo, Jing & Lin, Penghui & Zhang, Limao & Pan, Yue & Xiao, Zhonghua, 2023. "Dynamic adaptive encoder-decoder deep learning networks for multivariate time series forecasting of building energy consumption," Applied Energy, Elsevier, vol. 350(C).
    5. Pengying Wang & Shuo Zhang, 2022. "Retrofitting Strategies Based on Orthogonal Array Testing to Develop Nearly Zero Energy Buildings," Sustainability, MDPI, vol. 14(8), pages 1-18, April.
    6. Pan, Yue & Zhang, Limao, 2020. "Data-driven estimation of building energy consumption with multi-source heterogeneous data," Applied Energy, Elsevier, vol. 268(C).
    7. Joana Fernandes & Maria Catarina Santos & Rui Castro, 2021. "Introductory Review of Energy Efficiency in Buildings Retrofits," Energies, MDPI, vol. 14(23), pages 1-18, December.
    8. Li, Qing & Zhang, Lianying & Zhang, Limao & Wu, Xianguo, 2021. "Optimizing energy efficiency and thermal comfort in building green retrofit," Energy, Elsevier, vol. 237(C).

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