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Energy simulation modeling and savings analysis of load sharing between house and office

Author

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  • Cho, Soolyeon
  • Lee, Kwang Ho
  • Kang, Eun Chul
  • Lee, Euy Joon

Abstract

This paper presents the potential benefits of the thermal load sharing between the two different building types such as residential house and commercial office building through the process of energy simulation modeling. Both the house and office simulation models have the same geometries with the conditioned spaces of 200 m2 each for the weather conditions of Seoul, South Korea. This study shows and analyzes the thermal energy demand and consumption results simulated from the four different scenarios using the EnergyPlus V6.0 thermal simulation program; i.e., Case-1) a house with conventional heating and cooling systems, Case-2) an office with conventional heating and cooling systems, and Case-3) a simple sum of the two cases (i.g., Case-2 + Case-3), and Case-4) a load sharing model that provides heating and cooling to both the house and the office using combined HVAC systems. This paper evaluates the thermal energy consumption patterns and potential benefits of the load sharing system compared to the conventional systems. The optimal system configurations of the load sharing systems are proposed. In conclusion, this paper discusses the potential issues and challenges for implementing the load sharing systems as well as the possible solutions for these issues.

Suggested Citation

  • Cho, Soolyeon & Lee, Kwang Ho & Kang, Eun Chul & Lee, Euy Joon, 2013. "Energy simulation modeling and savings analysis of load sharing between house and office," Renewable Energy, Elsevier, vol. 54(C), pages 70-77.
    • Handle: RePEc:eee:renene:v:54:y:2013:i:c:p:70-77
    DOI: 10.1016/j.renene.2012.08.058
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    Cited by:

    1. Spandagos, Constantinos & Ng, Tze Ling, 2017. "Equivalent full-load hours for assessing climate change impact on building cooling and heating energy consumption in large Asian cities," Applied Energy, Elsevier, vol. 189(C), pages 352-368.
    2. Zhao, Deyin & Zhong, Ming & Zhang, Xu & Su, Xing, 2016. "Energy consumption predicting model of VRV (Variable refrigerant volume) system in office buildings based on data mining," Energy, Elsevier, vol. 102(C), pages 660-668.
    3. Hirvonen, Janne & Kayo, Genku & Hasan, Ala & Sirén, Kai, 2014. "Local sharing of cogeneration energy through individually prioritized controls for increased on-site energy utilization," Applied Energy, Elsevier, vol. 135(C), pages 350-363.

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