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Distributed model predictive control for building HVAC systems: a case study.

Number: pap. 3611

Author(s) : PUTTA V., KIM D., CAI J., et al.

Summary

Model predictive control (MPC) in building HVAC systems incorporates predictions of weather and occupancy to determine the optimal operating setpoints. However, application of MPC strategies to large buildings might not be feasible in real time due to the large number of degrees of freedom in the underlying optimization problem. Decomposing the problem into several smaller sub-problems to be solved in parallel is one way to circumvent the high computational requirements. Such an approach, termed Distributed MPC, requires certain approximations about the underlying sub-problems to converge to a consistent solution thus leading to a trade-off between computational load and optimality. In this paper, we present a simulation-based evaluation for a Distributed MPC formulation for a case study based on a medium-sized commercial building. Results indicate that distributed MPC can offer near-optimal control at a fraction of the computational time that centralized MPC requires while maintaining occupant comfort.

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Pages: 8 p.

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Details

  • Original title: Distributed model predictive control for building HVAC systems: a case study.
  • Record ID : 30013806
  • Languages: English
  • Subject: Environment
  • Source: 2014 Purdue Conferences. 3rd International High Performance Buildings Conference at Purdue.
  • Publication date: 2014/07/14

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