Korean Journal of Air-Conditioning and Refrigeration Engineering (설비공학논문집)

The Society of Air-Conditioning and Refrigerating Engineers of Korea (대한설비공학회)
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A Case Study on Energy Performance Analysis of Retrofitted Building Using Inverse Model Toolkit

Inverse Model Toolkit을 이용한 리모델링 건축물의 에너지 성능평가 사례

  • Kwon, Kyung-Woo (Graduate School of Architectural Engineering, Hanyang University) ;
  • Lee, Suk-Joo (Graduate School of Architectural Engineering, Hanyang University) ;
  • Park, Jun-Seok (Department of Architectural Engineering, Hanyang University)
  • 권경우 (한양대학교 대학원 건축공학과) ;
  • 이석주 (한양대학교 대학원 건축공학과) ;
  • 박준석 (한양대학교 건축공학과)
  • Received : 2014.06.19
  • Accepted : 2014.07.03
  • Published : 2014.08.10
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Abstract

Several models and methods have been developed to verify the improvement of energy performance in retrofit buildings. The verification is important to confirm the effectiveness of new technologies or retrofits. Inverse model toolkit proposed by ASHRAE evaluates the changes of the energy performance of retrofit buildings by using actual energy consumption data. In this study, the inverse model toolkit was used to analyze heating and cooling energy performance of an office building. Analyzed coefficients of correlation of actual energy consumption with estimated energy consumption was above 0.92 and well fitted. It was confirmed that energy consumption of natural gas decreased by 43.4% and also that electricity decreased by 13.8%, after the retrofit of the case building. For the energy usage, cooling energy was increased by 7.4%, heating energy was decreased by 42.3%, hot water and cooking were increased by 3.4%, lighting and electronics were decreased by 19.3%, and the total energy was decreased by 18.9%.

Keywords

References

  1. Statistics Korea, Korean statistical information service, 2013, Statistics of building.
  2. Statistics Korea, Korean statistical information service, 2013, Statistics of building permission commencement works.
  3. Christensen, C., Roberts, D., Polly, B., Bianchi, M., Kruis, N., and Anderson, R., 2010, Building energy simulation accuracy, National Renewable Energy Laboratory.
  4. ASHRAE, 2002, Guideline 14-2002-Measurement of energy and demand savings, American Society of Refrigerating and Air-Conditioning Engineers.
  5. Haberl, J. S. and Cho, S., 2004, Literature review of uncertainty of analysis methods(Inverse Model Toolkit), Report to the Texas Commission on Environmental Quality.
  6. Goldberg, M. L., 1982, A geometrical approach to nondifferentiable regression models as related to methods for assessing residential energy conservation, Ph.D. thesis, Princeton University, NJ.
  7. Fels, M. F., 1986, PRISM : An introduction, Energy and Buildings, Vol. 9, No. 1-2, pp. 5-18. https://doi.org/10.1016/0378-7788(86)90003-4
  8. Fels, M. F., Kissock, J. K., Marean, M. A., and Reynolds, C., 1995, PRISM (Advanced Version 1.0) Users Guide, Center for Energy and Environmental Studies, Princeton University.
  9. Schrock, D. W. and Claridge, D. E., 1989, Predicting energy usage in a supermarket, Proceedings, Sixth Symposium on Improving Building Systems in Hot and Humid Climates, Texas A&M University, Dallas, pp. 44-54.
  10. Ruch, D. and Claridge, D. E., 1992, A four-parameter change-point model for predicting energy consumption in commercial buildings, Journal of Solar Energy Engineering-transactions of The ASME, Vol. 114, No. 2, pp. 77-83. https://doi.org/10.1115/1.2929993
  11. Kissock, J. K., 1993, A methodology to measure energy savings in commercial buildings, Ph.D. theis, Texas A&M University, College Station, TX.
  12. Kissock, J. K., 1994, Modeling commercial building energy use with artificial neural networks, Proceedings, 29th Intersociety Energy Conversion Engineering Conference, Vol. 3, pp. 1290-1295, Monterey, CA.
  13. Kissock, J. K., Xun, W., Sparks, R., Claridge, D., Mahoney, J., and Haberl, J., 1994, EModel Version 1.4de, Texas A&M University, College Station, TX.
  14. Ruch, D., Chen, L., Haberl, J., and Claridge, D. E., 1993, A change-point principal component analysis (CP/PCA) method for predicting energy usage in commercial buildings : The PCA Model, Journal of Solar Energy Engineering-transactions of The ASME, Vol. 115, No. 2, pp. 77-84. https://doi.org/10.1115/1.2930035
  15. Ruch, D. and Claridge, D. E., 1993, A development and comparison of NAC estimates for linear and changepoint energy models for commercial buildings, Energy and Buildings, Vol. 20, No. 1, pp. 87-95. https://doi.org/10.1016/0378-7788(93)90041-R
  16. Rabl, A. and Rialhe, A., 1992, Energy signature models for commercial buildings : test with measured data and interpretation, Energy and Buildings, Vol. 19, No. 2, pp. 143-154. https://doi.org/10.1016/0378-7788(92)90008-5
  17. Rabl, A., Norford, L., and Spadaro, J., 1992, Steady state models for analysis of commercial building energy data, Proceedings, ACEEE Summer Study on Energy Efficiency in Buildings, Pacific Grove, CA, pp. 9.239-9.261.
  18. Ruch, D. and Claridge, D. E., 1992, A four-parameter change-point model for predicting energy consumption in commercial buildings, Journal of Solar Energy Engineering-transactions of The ASME, Vol. 114, No. 2, pp. 77-83. https://doi.org/10.1115/1.2929993
  19. Kissock, J. K., Haberl, J. S., and Claridge, D. E., 2002, Inverse Modeling Toolkit : Numerical Algorithms, ASHRAE KC-03-2-1(RP-1050).

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