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Design and application of distributed economic model predictive control for large-scale building temperature regulation.

Number: pap. 3236

Author(s) : PATEL N. R., RAWLINGS J. B., WENZEL M. J., et al.

Summary

Although recent research has suggested model predictive control as a promising solution for minimizing energy costs of commercial buildings, advanced control systems have not been widely deployed in practice. Large-scale implementations, including industrial complexes and university campuses, may contain thousands of air handler units each serving a multiplicity of zones. A single centralized control system for these applications is not desirable. In this paper, we propose a distributed control system to economically optimize temperature regulation for large-scale commercial building applications. The decomposition strategy considers the complexities of thermal energy storage, zone interactions, and chiller plant equipment while remaining computationally tractable. One of the primary bene?ts of the proposed formulation is that the low-level air side problem can be decoupled and solved in a distributed manner; hence, it can be easily extended to handle large applications. Peak demand charges, a major source of coupling, are included. The interactions of the air side system with the water side system a real so considered, including discrete decisions, such as turning chillers on and off. To deploy such a control scheme, a system model is required. Since using physical knowledge about building model scan greatly reduce the number of parameters that must be identi?ed, grey-boxmodels are recommended to reduce the length of expensive identi?cation testing. We demonstrate the effectiveness of this control system architecture and identi?cation procedure via simulation studies.

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Pages: 10 p.

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Details

  • Original title: Design and application of distributed economic model predictive control for large-scale building temperature regulation.
  • Record ID : 30019282
  • Languages: English
  • Source: 2016 Purdue Conferences. 4th International High Performance Buildings Conference at Purdue.
  • Publication date: 2016/07/11

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