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Energy retrofit analysis toolkits for commercial buildings: A review

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  • Lee, Sang Hoon
  • Hong, Tianzhen
  • Piette, Mary Ann
  • Taylor-Lange, Sarah C.

Abstract

Retrofit analysis toolkits can be used to optimize energy or cost savings from retrofit strategies, accelerating the adoption of ECMs (energy conservation measures) in buildings. This paper provides an up-to-date review of the features and capabilities of 18 energy retrofit toolkits, including ECMs and the calculation engines. The fidelity of the calculation techniques, a driving component of retrofit toolkits, were evaluated. An evaluation of the issues that hinder effective retrofit analysis in terms of accessibility, usability, data requirement, and the application of efficiency measures, provides valuable insights into advancing the field forward. Following this review the general concepts were determined: (1) toolkits developed primarily in the private sector use empirically data-driven methods or benchmarking to provide ease of use, (2) almost all of the toolkits which used EnergyPlus or DOE-2 were freely accessible, but suffered from complexity, longer data input and simulation run time, (3) in general, there appeared to be a fine line between having too much detail resulting in a long analysis time or too little detail which sacrificed modeling fidelity. These insights provide an opportunity to enhance the design and development of existing and new retrofit toolkits in the future.

Suggested Citation

  • Lee, Sang Hoon & Hong, Tianzhen & Piette, Mary Ann & Taylor-Lange, Sarah C., 2015. "Energy retrofit analysis toolkits for commercial buildings: A review," Energy, Elsevier, vol. 89(C), pages 1087-1100.
    • Handle: RePEc:eee:energy:v:89:y:2015:i:c:p:1087-1100
    DOI: 10.1016/j.energy.2015.06.112
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    References listed on IDEAS

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    1. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    2. Zhao, Hai-xiang & Magoulès, Frédéric, 2012. "A review on the prediction of building energy consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3586-3592.
    3. Pacheco, R. & Ordóñez, J. & Martínez, G., 2012. "Energy efficient design of building: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(6), pages 3559-3573.
    4. Ranjan K. Bose, 2010. "Energy Efficient Cities : Assessment Tools and Benchmarking Practices," World Bank Publications - Books, The World Bank Group, number 2449, December.
    5. Remer, Donald S. & Nieto, Armando P., 1995. "A compendium and comparison of 25 project evaluation techniques. Part 1: Net present value and rate of return methods," International Journal of Production Economics, Elsevier, vol. 42(1), pages 79-96, November.
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