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Distributed Model Predictive Control for Forced-Air Systems

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Intelligent Building Control Systems

Part of the book series: Advances in Industrial Control ((AIC))

Abstract

This chapter focuses on advanced control design, specifically for forced air HVAC systems. Such advanced control schemes incorporate predictions of weather, occupancy, renewable energy availability, and energy price signals in order to deliver performance-driven automated decision making at a hierarchy of levels. The chapter covers thermal modeling for controls, predictive control design, and implementation of such controllers in real-world buildings. An overview of standard computational platforms and communication systems in buildings is reported. Our main objective is to discuss how advanced control relates to the existing building practices; in particular, a distributed control logic “Trim and Respond” is described in detail. The “Trim and Respond” logic is shown to match a one-step explicit distributed model predictive controller. The chapter concludes with an algorithm for advanced distributed model predictive control that is implementable on existing distributed building control architectures.

Sections 7.4.1, 7.6.1, 7.6. include material reused with permission from [1] (©2013 EUCA), Sect. 7.6.3 includes material reused with permission from [2] (©2015 AACC) and [3] (©2016 John Wiley & Sons, Ltd.).

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Acknowledgements

This material is based upon work supported by the National Science Foundation under Grant No. 1239552. The U.S. Department of Energy (DOE) and the Department of Science and Technology (DST), Government of India (GOI) provided joint funding for work under the U.S.–India Partnership to Advance Clean Energy Research (PACE–R) program’s “U.S.–India Joint Center for Building Energy Research and Development” (CBERD) project. The Assistant Secretary for Energy Efficiency and Renewable Energy, Office of Building Technology, State and Community Programs, of the U.S. DOE under Contract No. DE-AC02- 05CH11231 supports the U.S. CBERD activity. The DST, GOI, administered by Indo-U.S. Science and Technology Forum, supports the Indian CBERD activity. The authors acknowledge Dr Philip Haves of Lawrence Berkeley National Laboratory for his advice and continued support.

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Authors and Affiliations

  1. University of California, Berkeley, CA, USA

    Sarah M. Koehler, Frank Chuang & Francesco Borrelli

  2. NEXTracker, Inc., Fremont, CA, USA

    Yudong Ma & Allan Daly

Authors
  1. Sarah M. Koehler
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  2. Frank Chuang
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  3. Yudong Ma
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  4. Allan Daly
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  5. Francesco Borrelli
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Corresponding author

Correspondence to Sarah M. Koehler .

Editor information

Editors and Affiliations

  1. Department of Electrical, Computer, and Systems Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA

    John T. Wen

  2. Department of Mechanical, Aerospace, and Nuclear Engineering, Rensselaer Polytechnic Institute, Troy, New York, USA

    Sandipan Mishra

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Koehler, S.M., Chuang, F., Ma, Y., Daly, A., Borrelli, F. (2018). Distributed Model Predictive Control for Forced-Air Systems. In: Wen, J., Mishra, S. (eds) Intelligent Building Control Systems. Advances in Industrial Control. Springer, Cham. https://doi.org/10.1007/978-3-319-68462-8_7

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  • DOI: https://doi.org/10.1007/978-3-319-68462-8_7

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-68461-1

  • Online ISBN: 978-3-319-68462-8

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