Modelling and PID Control of HVAC System According to Energy Efficiency and Comfort Criteria

  • Carlos Blasco
  • Javier Monreal
  • Ignacio Benítez
  • Andrés Lluna
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 12)

Abstract

This article describes the work being developed concerning the optimization of climate conditions in office buildings by the use of modeling and simulation tools to define the building’s energy demand, and the design and implementation of PID controls for the different control areas of the HVAC system, whose parameters have been adjusted based on simulation. To perform these studies, different simulation tools have been combined to obtain a model of the entire system: design of building and building’s energy demand in HVAC with Google SketchUp and EnergyPlus using a complete year of simulation with weather conditions of the building’s location, and HVAC system modeling with Dymola. These different tools have been combined with BCVTB co-simulation platform. A physical implementation is being finished by the deployment of distributed temperature and humidity sensors along the building. The measured data will be used to fine tune the PID controller parameters previously designed in the modeling stage.

Keywords

Heat Pump Building Energy National Renewable Energy Laboratory HVAC System Modeling Building Energy 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Reglamento de Instalaciones Térmicas en los Edificios (RITE). Modificación del Real Decreto 1826/2009 (in Spanish)Google Scholar
  2. 2.
    Building Controls Virtual Test Bed (BCVTB), https://gaia.lbl.gov/bcvtb
  3. 3.
    Lluna, A., Benítez, I., Monreal, J., Díaz, I.: Towards Zero Energy Balance in Tertiary Buildings. In: CMTEE 2010, Energy Technological Institute (2010)Google Scholar
  4. 4.
  5. 5.
    Efficiency Valuation Organization (EVO), www.evo-world.org
  6. 6.
    EnergyPlus, www.energyplus.gov
  7. 7.
    Crawley, D.B., et al.: Contrasting the capabilities of building energy performance simulation programs. Building and Environment 43, 661–673 (2008), Science directCrossRefGoogle Scholar
  8. 8.
    Loutzenhiser, P.G., Manz, H., et al.: An empirical validation of window solar gain models and the associated interactions. International Journal of Thermal Sciences 48, 85–95 (2009)CrossRefGoogle Scholar
  9. 9.
    Plan de Acción 2008/2012 de la Estrategia de Ahorro y Eficiencia Energética en España (PAE4+) (in Spanish)Google Scholar
  10. 10.
  11. 11.
  12. 12.
    Google SketchUp, http://sketchup.google.com/
  13. 13.
    Ellis, P.G., Torcellin, P.A.: Energy Design Plugin: An EnergyPlus Plugin for SketchUp. In: IBPSA-USA SimBuild 2008 Conference, National Renewable Energy Laboratory (2008)Google Scholar
  14. 14.
    Wetter, M.: Buildings, https://gaia.lbl.gov/bir
  15. 15.
    Wetter, M.: Modelica-based Modeling and Simulation to Support Research and Development in Building Energy and Control Systems. Journal of Building Performance Simulation 2(2), 143–161 (2009)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Carlos Blasco
    • 1
  • Javier Monreal
    • 1
  • Ignacio Benítez
    • 1
  • Andrés Lluna
    • 1
  1. 1.Energy Technological InstitutePaternaSpain

Personalised recommendations