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Model calibration for building energy efficiency simulation

Author

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  • Mustafaraj, Giorgio
  • Marini, Dashamir
  • Costa, Andrea
  • Keane, Marcus

Abstract

This research work deals with an Environmental Research Institute (ERI) building where an underfloor heating system and natural ventilation are the main systems used to maintain comfort condition throughout 80% of the building areas. Firstly, this work involved developing a 3D model relating to building architecture, occupancy & HVAC operation. Secondly, the calibration methodology, which consists of two levels, was then applied in order to insure accuracy and reduce the likelihood of errors. To further improve the accuracy of calibration a historical weather data file related to year 2011, was created from the on-site local weather station of ERI building. After applying the second level of calibration process, the values of Mean bias Error (MBE) and Cumulative Variation of Root Mean Squared Error (CV(RMSE)) on hourly based analysis for heat pump electricity consumption varied within the following ranges: (MBE)hourly from −5.6% to 7.5% and CV(RMSE)hourly from 7.3% to 25.1%. Finally, the building was simulated with EnergyPlus to identify further possibilities of energy savings supplied by a water to water heat pump to underfloor heating system. It found that electricity consumption savings from the heat pump can vary between 20% and 27% on monthly bases.

Suggested Citation

  • Mustafaraj, Giorgio & Marini, Dashamir & Costa, Andrea & Keane, Marcus, 2014. "Model calibration for building energy efficiency simulation," Applied Energy, Elsevier, vol. 130(C), pages 72-85.
    • Handle: RePEc:eee:appene:v:130:y:2014:i:c:p:72-85
    DOI: 10.1016/j.apenergy.2014.05.019
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    References listed on IDEAS

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    1. Xu, Xiaoqi & Culligan, Patricia J. & Taylor, John E., 2014. "Energy Saving Alignment Strategy: Achieving energy efficiency in urban buildings by matching occupant temperature preferences with a building’s indoor thermal environment," Applied Energy, Elsevier, vol. 123(C), pages 209-219.
    2. Jain, Rishee K. & Smith, Kevin M. & Culligan, Patricia J. & Taylor, John E., 2014. "Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy," Applied Energy, Elsevier, vol. 123(C), pages 168-178.
    3. Ibrahim, Mohamad & Biwole, Pascal Henry & Wurtz, Etienne & Achard, Patrick, 2014. "Limiting windows offset thermal bridge losses using a new insulating coating," Applied Energy, Elsevier, vol. 123(C), pages 220-231.
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