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Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study

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  • Michailidis, Iakovos T.
  • Schild, Thomas
  • Sangi, Roozbeh
  • Michailidis, Panagiotis
  • Korkas, Christos
  • Fütterer, Johannes
  • Müller, Dirk
  • Kosmatopoulos, Elias B.

Abstract

A variety of novel, recyclable and reusable, construction materials has already been studied within literature during the past years, aiming at improving the overall energy efficiency ranking of the building envelope. However, several studies show that a delicate control of indoor climating elements can lead to a significant performance improvement by exploiting the building’s savings potential via smart adaptive HVAC regulation to exogenous uncertain disturbances (e.g. weather, occupancy). Building Optimization and Control (BOC) systems can be categorized into two different groups: centralized (requiring high data transmission rates at a central node from every corner of the overall system) and decentralized1The terms “decentralized”, “distributed” and “agent-based” are considered to have similar meanings herein.1 (assuming an intercommunication among neighboring constituent systems). Moreover, both approaches can be further divided into two subcategories, respectively: model-assisted (usually introducing modeling oversimplifications) and model-free (typically presenting poor stability and very slow convergence rates). This paper presents the application of a novel, decentralized, agent-based, model-free BOC methodology (abbreviated as L4GPCAO) to a modern non-residential building (E.ON. Energy Research Center’s main building), equipped with controllable HVAC systems and renewable energy sources by utilizing the existing Building Management System (BES). The building testbed is located inside the RWTH Aachen University campus in Aachen, Germany. A combined rule criterion composed of the non-renewable energy consumption (NREC) and the thermal comfort index – aligned to international comfort standards – was adopted in all cases presented herein. Besides the limited availability of the specified building testbed, real-life experiments demonstrated operational effectiveness of the proposed approach in BOC applications with complex, emerging dynamics arising from the building’s occupancy and thermal characteristics. L4GPCAO outperformed the control strategy that was designed by the planers and system provider, in a conventional manner, requiring no more than five test days.

Suggested Citation

  • Michailidis, Iakovos T. & Schild, Thomas & Sangi, Roozbeh & Michailidis, Panagiotis & Korkas, Christos & Fütterer, Johannes & Müller, Dirk & Kosmatopoulos, Elias B., 2018. "Energy-efficient HVAC management using cooperative, self-trained, control agents: A real-life German building case study," Applied Energy, Elsevier, vol. 211(C), pages 113-125.
    • Handle: RePEc:eee:appene:v:211:y:2018:i:c:p:113-125
    DOI: 10.1016/j.apenergy.2017.11.046
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    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. Dagmara Kociuba & Maciej Janczak, 2024. "Effects of the Disbursement of EU Cohesion Policy 2014–2020 Funds on Improving the Energy Efficiency of Buildings in Poland and Germany," Energies, MDPI, vol. 17(17), pages 1-23, September.
    3. 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.
    4. Su, Bing & Wang, Shengwei, 2020. "An agent-based distributed real-time optimal control strategy for building HVAC systems for applications in the context of future IoT-based smart sensor networks," Applied Energy, Elsevier, vol. 274(C).
    5. Muhammad Saidu Aliero & Muhammad Asif & Imran Ghani & Muhammad Fermi Pasha & Seung Ryul Jeong, 2022. "Systematic Review Analysis on Smart Building: Challenges and Opportunities," Sustainability, MDPI, vol. 14(5), pages 1-28, March.
    6. 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.
    7. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    8. Esmaeilzadeh, Ahmad & Deal, Brian & Yousefi-Koma, Aghil & Zakerzadeh, Mohammad Reza, 2023. "How combination of control methods and renewable energies leads a large commercial building to a zero-emission zone – A case study in U.S," Energy, Elsevier, vol. 263(PD).
    9. Wei, Congying & Xu, Jian & Liao, Siyang & Sun, Yuanzhang & Jiang, Yibo & Ke, Deping & Zhang, Zhen & Wang, Jing, 2018. "A bi-level scheduling model for virtual power plants with aggregated thermostatically controlled loads and renewable energy," Applied Energy, Elsevier, vol. 224(C), pages 659-670.
    10. Charalampos Rafail Lazaridis & Iakovos Michailidis & Georgios Karatzinis & Panagiotis Michailidis & Elias Kosmatopoulos, 2024. "Evaluating Reinforcement Learning Algorithms in Residential Energy Saving and Comfort Management," Energies, MDPI, vol. 17(3), pages 1-33, January.
    11. Temitope Omotayo & Alireza Moghayedi & Bankole Awuzie & Saheed Ajayi, 2021. "Infrastructure Elements for Smart Campuses: A Bibliometric Analysis," Sustainability, MDPI, vol. 13(14), pages 1-32, July.
    12. Li, Wenzhuo & Wang, Shengwei, 2020. "A multi-agent based distributed approach for optimal control of multi-zone ventilation systems considering indoor air quality and energy use," Applied Energy, Elsevier, vol. 275(C).
    13. Su, Bing & Wang, Shengwei & Li, Wenzhuo, 2021. "Impacts of uncertain information delays on distributed real-time optimal controls for building HVAC systems deployed on IoT-enabled field control networks," Applied Energy, Elsevier, vol. 300(C).
    14. 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.
    15. Baldi, Simone & Zhang, Fan & Le Quang, Thuan & Endel, Petr & Holub, Ondrej, 2019. "Passive versus active learning in operation and adaptive maintenance of Heating, Ventilation, and Air Conditioning," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    16. Panagiotis Michailidis & Iakovos Michailidis & Socratis Gkelios & Elias Kosmatopoulos, 2024. "Artificial Neural Network Applications for Energy Management in Buildings: Current Trends and Future Directions," Energies, MDPI, vol. 17(3), pages 1-47, January.

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