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Adaptive modeling for reliability in optimal control of complex HVAC systems

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Abstract

The model-based real-time optimization (MRTO) of heating, ventilation, and air-conditioning (HVAC) systems is an efficient tool for improving energy efficiency and for effective operation. Model-based real-time optimization of HVAC systems needs to regularly optimize the set points for local-loop operation, taking into account the interactions between HVAC components with the help of system-performance models. MRTO relies on the accuracy of the performance model to provide reliability in decision making. In practice, due to high diversity in ambient conditions and load demands, system-model mismatches are difficult to avoid. This paper presents an adaptive, model-based, real-time optimization (AMRTO) approach for large-scale, complex HVAC systems, to counter any model mismatches by updating the performance model in real time with real-time measurements. Furthermore, to make this approach practically applicable and to keep the online training process computationally manageable, an empirical-physical model of HVAC system components was set up that is suitable for online training, and hybrid genetic algorithms (HGAs) method was used for faster, yet reliable, online training of the performance model. A case study was used to evaluate the performance of the proposed approach. The results demonstrated that the proposed AMRTO was able to provide energy saving approximately 8% and reduce the online computational burden by 99%.

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Acknowledgements

The authors would like to thank Dr. Gongsheng Huang at the City University of Hong Kong for his assistance. The work described in this paper was supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project No. 11209518).

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Authors and Affiliations

  1. Department of Architecture and Civil Engineering, City University of Hong Kong, Kowloon, Hong Kong, China

    Hussain Syed Asad & Richard Kwok Kit Yuen

  2. Department of Chemical and Materials Engineering, University of Alberta, Edmonton, AB, Canada

    Jinfeng Liu

  3. School of Environmental Science and Engineering, Suzhou University of Science and Technology, Suzhou, China

    Junqi Wang

Authors
  1. Hussain Syed Asad
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  2. Richard Kwok Kit Yuen
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  3. Jinfeng Liu
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  4. Junqi Wang
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Correspondence to Hussain Syed Asad.

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Asad, H.S., Yuen, R.K.K., Liu, J. et al. Adaptive modeling for reliability in optimal control of complex HVAC systems. Build. Simul. 12, 1095–1106 (2019). https://doi.org/10.1007/s12273-019-0558-9

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  • DOI: https://doi.org/10.1007/s12273-019-0558-9

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