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Recurrent neural networkbased economic MPC applied to building HVAC systems.

Number: 3649

Author(s) : ELLIS M. J., CHINDE V.

Summary

Numerous studies have demonstrated the potential benefit of economic model predictive control (EMPC) applied to building heating, ventilation, and air conditioning (HVAC) systems. However, technological barriers preventing largescale adoption of EMPC to building HVAC systems exist including model construction and training. In this work, an encoder decoder long short term memory based EMPC framework is developed. The encoderdecoder model, a datadriven modeling approach, offers advantages in model construction and training over conventional greybox modeling approaches. The EMPC objective is to minimize the HVAC operating utility cost by manipulating the building zone temperature setpoints. Closedloop simulations under the EMPC using EnergyPlus are employed to demonstrate the approach on a prototypical fivezone commercial building.

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Pages: 10

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Details

  • Original title: Recurrent neural networkbased economic MPC applied to building HVAC systems.
  • Record ID : 30028663
  • Languages: English
  • Subject: Technology
  • Source: 2021 Purdue Conferences. 6th International High Performance Buildings Conference at Purdue.
  • Publication date: 2021/05/24
  • Document available for consultation in the library of the IIR headquarters only.

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