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Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings

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  • Chung, William

Abstract

Benchmarking systems from a sample of reference buildings need to be developed to conduct benchmarking processes for the energy efficiency of commercial buildings. However, not all benchmarking systems can be adopted by public users (i.e., other non-reference building owners) because of the different methods in developing such systems.

Suggested Citation

  • Chung, William, 2012. "Using the fuzzy linear regression method to benchmark the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 95(C), pages 45-49.
    • Handle: RePEc:eee:appene:v:95:y:2012:i:c:p:45-49
    DOI: 10.1016/j.apenergy.2012.01.061
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    References listed on IDEAS

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    1. Chung, William & Hui, Y.V. & Lam, Y. Miu, 2006. "Benchmarking the energy efficiency of commercial buildings," Applied Energy, Elsevier, vol. 83(1), pages 1-14, January.
    2. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    3. Tanaka, Hideo & Hayashi, Isao & Watada, Junzo, 1989. "Possibilistic linear regression analysis for fuzzy data," European Journal of Operational Research, Elsevier, vol. 40(3), pages 389-396, June.
    4. Hojati, Mehran & Bector, C. R. & Smimou, Kamal, 2005. "A simple method for computation of fuzzy linear regression," European Journal of Operational Research, Elsevier, vol. 166(1), pages 172-184, October.
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    Cited by:

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    2. Moya, Diego & Torres, Roberto & Stegen, Sascha, 2016. "Analysis of the Ecuadorian energy audit practices: A review of energy efficiency promotion," Renewable and Sustainable Energy Reviews, Elsevier, vol. 62(C), pages 289-296.
    3. Ascione, Fabrizio & Bianco, Nicola & de’ Rossi, Filippo & Turni, Gianluca & Vanoli, Giuseppe Peter, 2013. "Green roofs in European climates. Are effective solutions for the energy savings in air-conditioning?," Applied Energy, Elsevier, vol. 104(C), pages 845-859.
    4. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    5. Xia, X.H. & Hu, Y. & Chen, G.Q. & Alsaedi, A. & Hayat, T. & Wu, X.D., 2015. "Vertical specialization, global trade and energy consumption for an urban economy: A value added export perspective for Beijing," Ecological Modelling, Elsevier, vol. 318(C), pages 49-58.
    6. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
    7. Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.
    8. Tamer, Tolga & Gürsel Dino, Ipek & Meral Akgül, Cagla, 2022. "Data-driven, long-term prediction of building performance under climate change: Building energy demand and BIPV energy generation analysis across Turkey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 162(C).
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    10. Zhao, Yang & Li, Tingting & Zhang, Xuejun & Zhang, Chaobo, 2019. "Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future," Renewable and Sustainable Energy Reviews, Elsevier, vol. 109(C), pages 85-101.
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    12. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.

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