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Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting

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  • Wang, Endong
  • Alp, Neslihan
  • Shi, Jonathan
  • Wang, Chao
  • Zhang, Xiaodong
  • Chen, Hong

Abstract

Enabling robust energy benchmarking to reliably locate performance inefficiency for upgrading is critical to the success of building retrofitting programs in building sector. Multi-criteria benchmarking is emerging as a more rational option over the traditional single-angle method to assess building performance which is fundamentally of multi-factor nature. Particularly, with its easier concept, the compromise Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) based multi-angle benchmarking appears attractive. Nevertheless, existing TOPSIS based procedures tend to ignore the common issue of multicollinearity trap which could result in misleading decisions. Meanwhile, variable clustering renders an empirical alternative for handling multicollinearity with high traceability. Combining with information-oriented Shannon entropy, this paper develops an iterative Clustering around Latent Variables (CLV) based objective entropy weighted TOPSIS approach for benchmarking building energy performance in a multi-factor manner. It essentially integrates the benefits of variable clustering to address multicollinearity with information theory for objective weighting on decision attributes in order to pursue TOPSIS benchmarking accuracy. A 324-dwelling case shows the robustness of the constructed procedure in a temporally dynamic context.

Suggested Citation

  • Wang, Endong & Alp, Neslihan & Shi, Jonathan & Wang, Chao & Zhang, Xiaodong & Chen, Hong, 2017. "Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting," Energy, Elsevier, vol. 125(C), pages 197-210.
    • Handle: RePEc:eee:energy:v:125:y:2017:i:c:p:197-210
    DOI: 10.1016/j.energy.2017.02.131
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    1. Glenn Milligan & Martha Cooper, 1985. "An examination of procedures for determining the number of clusters in a data set," Psychometrika, Springer;The Psychometric Society, vol. 50(2), pages 159-179, June.
    2. Mardani, Abbas & Zavadskas, Edmundas Kazimieras & Streimikiene, Dalia & Jusoh, Ahmad & Nor, Khalil M.D. & Khoshnoudi, Masoumeh, 2016. "Using fuzzy multiple criteria decision making approaches for evaluating energy saving technologies and solutions in five star hotels: A new hierarchical framework," Energy, Elsevier, vol. 117(P1), pages 131-148.
    3. Stein, Jeff Ross & Meier, Alan, 2000. "Accuracy of home energy rating systems," Energy, Elsevier, vol. 25(4), pages 339-354.
    4. Vu, D.H. & Muttaqi, K.M. & Agalgaonkar, A.P., 2015. "A variance inflation factor and backward elimination based robust regression model for forecasting monthly electricity demand using climatic variables," Applied Energy, Elsevier, vol. 140(C), pages 385-394.
    5. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    6. Benedetti, Miriam & Cesarotti, Vittorio & Introna, Vito & Serranti, Jacopo, 2016. "Energy consumption control automation using Artificial Neural Networks and adaptive algorithms: Proposal of a new methodology and case study," Applied Energy, Elsevier, vol. 165(C), pages 60-71.
    7. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
    8. Wanke, Peter & Pestana Barros, Carlos & Chen, Zhongfei, 2015. "An analysis of Asian airlines efficiency with two-stage TOPSIS and MCMC generalized linear mixed models," International Journal of Production Economics, Elsevier, vol. 169(C), pages 110-126.
    9. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    10. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
    11. B. Srdjevic & Y. Medeiros & A. Faria, 2004. "An Objective Multi-Criteria Evaluation of Water Management Scenarios," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 18(1), pages 35-54, February.
    12. Chavent, Marie & Kuentz-Simonet, Vanessa & Liquet, Benoît & Saracco, Jérôme, 2012. "ClustOfVar: An R Package for the Clustering of Variables," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 50(i13).
    13. 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.
    14. Lee, Wen-Shing & Lin, Yeong-Chuan, 2011. "Evaluating and ranking energy performance of office buildings using Grey relational analysis," Energy, Elsevier, vol. 36(5), pages 2551-2556.
    Full references (including those not matched with items on IDEAS)

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