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Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study

Author

Listed:
  • Iakovos T. Michailidis

    (Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece)

  • Roozbeh Sangi

    (Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany
    Bosch Thermotechnik GmbH, 73243 Wernau, Germany)

  • Panagiotis Michailidis

    (Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece
    Electrical and Computer Engineering Department, Polytechnic School of Xanthi, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Thomas Schild

    (Drees & Sommer Advanced Building Technologies GmbH, 70569 Stuttgart, Germany)

  • Johannes Fuetterer

    (Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany
    Aedifion GmbH, 50672 Cologne, Germany)

  • Dirk Mueller

    (Institute for Energy Efficient Buildings and Indoor Climate, E.ON. Energy Research Center, RWTH Aachen, 52074 Aachen, Germany)

  • Elias B. Kosmatopoulos

    (Information Technologies Institute (I.T.I.), Centre for Research & Technology—Hellas (CE.R.T.H.), 57001 Thessaloniki, Greece
    Electrical and Computer Engineering Department, Polytechnic School of Xanthi, Democritus University of Thrace, 67100 Xanthi, Greece)

Abstract

Modern literature exhibits numerous centralized control approaches—event-based or model assisted—for tackling poor energy performance in buildings. Unfortunately, even novel building optimization and control (BOC) strategies commonly suffer from complexity and scalability issues as well as uncertain behavior as concerns large-scale building ecosystems—a fact that hinders their practical compatibility and broader applicability. Moreover, decentralized optimization and control approaches trying to resolve scalability and complexity issues have also been proposed in literature. Those approaches usually suffer from modeling issues, utilizing an analytically available formula for the overall performance index. Motivated by the complications in existing strategies for BOC applications, a novel, decentralized, optimization and control approach—referred to as Local for Global Parameterized Cognitive Adaptive Optimization (L4GPCAO)—has been extensively evaluated in a simulative environment, contrary to previous constrained real-life studies. The current study utilizes an elaborate simulative environment for evaluating the efficiency of L4GPCAO; extensive simulation tests exposed the efficiency of L4GPCAO compared to the already evaluated centralized optimization strategy (PCAO) and the commercial control strategy that is adopted in the BOC practice (common reference case). L4GPCAO achieved a quite similar performance in comparison to PCAO (with 25% less control parameters at a local scale), while both PCAO and L4GPCAO significantly outperformed the reference BOC practice.

Suggested Citation

  • Iakovos T. Michailidis & Roozbeh Sangi & Panagiotis Michailidis & Thomas Schild & Johannes Fuetterer & Dirk Mueller & Elias B. Kosmatopoulos, 2020. "Balancing Energy Efficiency with Indoor Comfort Using Smart Control Agents: A Simulative Case Study," Energies, MDPI, vol. 13(23), pages 1-28, November.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6228-:d:451654
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    References listed on IDEAS

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    Cited by:

    1. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    2. Panagiotis Michailidis & Paschalis Pelitaris & Christos Korkas & Iakovos Michailidis & Simone Baldi & Elias Kosmatopoulos, 2021. "Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study," Energies, MDPI, vol. 14(23), pages 1-25, November.
    3. Dimitrios Vamvakas & Panagiotis Michailidis & Christos Korkas & Elias Kosmatopoulos, 2023. "Review and Evaluation of Reinforcement Learning Frameworks on Smart Grid Applications," Energies, MDPI, vol. 16(14), pages 1-38, July.

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