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Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes

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  • Lee, Wen-Shing

Abstract

Benchmarking the energy performance for cooling purposes in buildings is an important tool for energy management. This paper proposes a novel index and develops a benchmarking process for energy performance for cooling purposes by means of data envelopment analysis and cooling degree hour method. The research begins by using climate data to calculate cooling degree hour and proceeds to build an index of total performance of energy for cooling purposes by linear regression method. Finally, data envelopment analysis is adopted to benchmark the energy performance for cooling purposes in buildings with the index of total performance of energy for cooling that has the effect of ventilation factors (floor area and number of occupants) removed. An application to office buildings in Taiwan is presented to illustrate the development and the use of the evaluating method.

Suggested Citation

  • Lee, Wen-Shing, 2010. "Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes," Energy, Elsevier, vol. 35(1), pages 50-54.
    • Handle: RePEc:eee:energy:v:35:y:2010:i:1:p:50-54
    DOI: 10.1016/j.energy.2009.08.026
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    Citations

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    Cited by:

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    3. Khoshroo, Alireza & Mulwa, Richard & Emrouznejad, Ali & Arabi, Behrouz, 2013. "A non-parametric Data Envelopment Analysis approach for improving energy efficiency of grape production," Energy, Elsevier, vol. 63(C), pages 189-194.
    4. Lee, Wen-Shing & Kung, Chung-Kuan, 2011. "Using climate classification to evaluate building energy performance," Energy, Elsevier, vol. 36(3), pages 1797-1801.
    5. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Improvements of the operational rating system for existing residential buildings," Applied Energy, Elsevier, vol. 193(C), pages 112-124.
    6. Goto, Mika & Otsuka, Akihiro & Sueyoshi, Toshiyuki, 2014. "DEA (Data Envelopment Analysis) assessment of operational and environmental efficiencies on Japanese regional industries," Energy, Elsevier, vol. 66(C), pages 535-549.
    7. Vaninsky, Alexander, 2010. "Prospective national and regional environmental performance: Boundary estimations using a combined data envelopment – stochastic frontier analysis approach," Energy, Elsevier, vol. 35(9), pages 3657-3665.
    8. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
    9. Livingston, Olga V. & Pulsipher, Trenton C. & Anderson, David M. & Vlachokostas, Alex & Wang, Na, 2018. "An analysis of utility meter data aggregation and tenant privacy to support energy use disclosure in commercial buildings," Energy, Elsevier, vol. 159(C), pages 302-309.
    10. Wang, Yang & Zhao, Fu-Yun & Kuckelkorn, Jens & Liu, Di & Liu, Li-Qun & Pan, Xiao-Chuan, 2014. "Cooling energy efficiency and classroom air environment of a school building operated by the heat recovery air conditioning unit," Energy, Elsevier, vol. 64(C), pages 991-1001.
    11. Mousavi-Avval, Seyed Hashem & Rafiee, Shahin & Mohammadi, Ali, 2011. "Optimization of energy consumption and input costs for apple production in Iran using data envelopment analysis," Energy, Elsevier, vol. 36(2), pages 909-916.

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