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Title: Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis
Authors: Li, Hong Xian
Li, Yan
Jiang, Boya
Zhang, Limao
Wu, Xianguo
Lin, Jingyi
Keywords: Engineering::Civil engineering
Issue Date: 2020
Source: Li, H. X., Li, Y., Jiang, B., Zhang, L., Wu, X. & Lin, J. (2020). Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis. Renewable Energy, 149, 1414-1423.
Project: M4082160.030
Journal: Renewable Energy
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.
ISSN: 0960-1481
DOI: 10.1016/j.renene.2019.10.143
Schools: School of Civil and Environmental Engineering 
Rights: © 2019 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
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