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Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort

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  • Mathews, E. H.
  • Arndt, D. C.
  • Piani, C. B.
  • van Heerden, E.

Abstract

Good HVAC control is often the most cost-effective option to improve the energy efficiency of a building. However the effect of changing the control strategy (i.e. effect on indoor comfort and energy consumption) is usually the most difficult to predict. To achieve this more easily, a new simulation tool, QUICKcontrol, was developed. In this paper, the new tool is used to investigate the energy savings potential in the Engineering Tower Building (ETB) of the University of Pretoria. The influence of reset control, economizer cycle combined with CO2 control on the outside air ventilation rate and better system start-stop times were investigated. The simulation models were firstly verified with actual measurements obtained from the existing system to confirm their accuracy for realistic control retrofit simulations. With the aid of the integrated simulation tool it was possible to predict savings of 491 MWh per year (34% building energy saving) by implementing these control strategies. These control strategies can be implemented in the building with a direct payback period of less than 9 months.

Suggested Citation

  • Mathews, E. H. & Arndt, D. C. & Piani, C. B. & van Heerden, E., 2000. "Developing cost efficient control strategies to ensure optimal energy use and sufficient indoor comfort," Applied Energy, Elsevier, vol. 66(2), pages 135-159, June.
    • Handle: RePEc:eee:appene:v:66:y:2000:i:2:p:135-159
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    References listed on IDEAS

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    1. Lebrun, J., 1994. "Simulation of HVAC systems," Renewable Energy, Elsevier, vol. 5(5), pages 1151-1158.
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    Cited by:

    1. Ifrah Tahir & Ali Nasir & Abdullah Algethami, 2022. "Optimal Control Policy for Energy Management of a Commercial Bank," Energies, MDPI, vol. 15(6), pages 1-19, March.
    2. Baldi, Simone & Yuan, Shuai & Endel, Petr & Holub, Ondrej, 2016. "Dual estimation: Constructing building energy models from data sampled at low rate," Applied Energy, Elsevier, vol. 169(C), pages 81-92.
    3. Yan, Huaxia & Pan, Yan & Li, Zhao & Deng, Shiming, 2018. "Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 219(C), pages 312-324.
    4. Ogunjuyigbe, A.S.O. & Ayodele, T.R. & Akinola, O.A., 2017. "User satisfaction-induced demand side load management in residential buildings with user budget constraint," Applied Energy, Elsevier, vol. 187(C), pages 352-366.
    5. Kusiak, Andrew & Tang, Fan & Xu, Guanglin, 2011. "Multi-objective optimization of HVAC system with an evolutionary computation algorithm," Energy, Elsevier, vol. 36(5), pages 2440-2449.
    6. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    7. Moudgil, Vipul & Hewage, Kasun & Hussain, Syed Asad & Sadiq, Rehan, 2023. "Integration of IoT in building energy infrastructure: A critical review on challenges and solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 174(C).
    8. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    9. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    10. Mossolly, M. & Ghali, K. & Ghaddar, N., 2009. "Optimal control strategy for a multi-zone air conditioning system using a genetic algorithm," Energy, Elsevier, vol. 34(1), pages 58-66.
    11. Shaikh, Pervez Hameed & Nor, Nursyarizal Bin Mohd & Nallagownden, Perumal & Elamvazuthi, Irraivan & Ibrahim, Taib, 2014. "A review on optimized control systems for building energy and comfort management of smart sustainable buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 34(C), pages 409-429.
    12. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
    13. Wei Wang & Xiaofang Shan & Syed Asad Hussain & Changshan Wang & Ying Ji, 2020. "Comparison of Multi-Control Strategies for the Control of Indoor Air Temperature and CO 2 with OpenModelica Modeling," Energies, MDPI, vol. 13(17), pages 1-20, August.
    14. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    15. Anatolijs Borodinecs & Arturs Palcikovskis & Vladislavs Jacnevs, 2022. "Indoor Air CO 2 Sensors and Possible Uncertainties of Measurements: A Review and an Example of Practical Measurements," Energies, MDPI, vol. 15(19), pages 1-15, September.
    16. Kusiak, Andrew & Li, Mingyang, 2010. "Cooling output optimization of an air handling unit," Applied Energy, Elsevier, vol. 87(3), pages 901-909, March.

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