IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i23p6228-d451654.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/23/6228/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/23/6228/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
    2. Lehmann, B. & Dorer, V. & Gwerder, M. & Renggli, F. & Tödtli, J., 2011. "Thermally activated building systems (TABS): Energy efficiency as a function of control strategy, hydronic circuit topology and (cold) generation system," Applied Energy, Elsevier, vol. 88(1), pages 180-191, January.
    3. Ueno, Tsuyoshi & Sano, Fuminori & Saeki, Osamu & Tsuji, Kiichiro, 2006. "Effectiveness of an energy-consumption information system on energy savings in residential houses based on monitored data," Applied Energy, Elsevier, vol. 83(2), pages 166-183, February.
    4. Olofsson, Thomas & Mahlia, T.M.I., 2012. "Modeling and simulation of the energy use in an occupied residential building in cold climate," Applied Energy, Elsevier, vol. 91(1), pages 432-438.
    5. Wu, Zhou & Wang, Bo & Xia, Xiaohua, 2016. "Large-scale building energy efficiency retrofit: Concept, model and control," Energy, Elsevier, vol. 109(C), pages 456-465.
    6. 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.
    7. Leckner, Mitchell & Zmeureanu, Radu, 2011. "Life cycle cost and energy analysis of a Net Zero Energy House with solar combisystem," Applied Energy, Elsevier, vol. 88(1), pages 232-241, January.
    8. Healy, John D. & Clinch, J. Peter, 2002. "Fuel poverty, thermal comfort and occupancy: results of a national household-survey in Ireland," Applied Energy, Elsevier, vol. 73(3-4), pages 329-343, November.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    2. 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.
    3. Francesco Mancini & Gianluigi Lo Basso & Livio de Santoli, 2019. "Energy Use in Residential Buildings: Impact of Building Automation Control Systems on Energy Performance and Flexibility," Energies, MDPI, vol. 12(15), pages 1-21, July.
    4. 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.
    5. Fujimi, Toshio & Kajitani, Yoshio & Chang, Stephanie E., 2016. "Effective and persistent changes in household energy-saving behaviors: Evidence from post-tsunami Japan," Applied Energy, Elsevier, vol. 167(C), pages 93-106.
    6. Handing Guo & Wanzhen Qiao & Jiren Liu, 2019. "Dynamic Feedback Analysis of Influencing Factors of Existing Building Energy-Saving Renovation Market Based on System Dynamics in China," Sustainability, MDPI, vol. 11(1), pages 1-16, January.
    7. Kristian Fabbri & Jacopo Gaspari & Laura Vandi, 2019. "Indoor Thermal Comfort of Pregnant Women in Hospital: A Case Study Evidence," Sustainability, MDPI, vol. 11(23), pages 1-24, November.
    8. Chen, Jiayu & Jain, Rishee K. & Taylor, John E., 2013. "Block Configuration Modeling: A novel simulation model to emulate building occupant peer networks and their impact on building energy consumption," Applied Energy, Elsevier, vol. 105(C), pages 358-368.
    9. Villa-Arrieta, Manuel & Sumper, Andreas, 2018. "A model for an economic evaluation of energy systems using TRNSYS," Applied Energy, Elsevier, vol. 215(C), pages 765-777.
    10. 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.
    11. Carolina Rodriguez & María Coronado & Marta D’Alessandro & Juan Medina, 2019. "The Importance of Standardised Data-Collection Methods in the Improvement of Thermal Comfort Assessment Models for Developing Countries in the Tropics," Sustainability, MDPI, vol. 11(15), pages 1-22, August.
    12. Chen, Han & Huang, Ye & Shen, Huizhong & Chen, Yilin & Ru, Muye & Chen, Yuanchen & Lin, Nan & Su, Shu & Zhuo, Shaojie & Zhong, Qirui & Wang, Xilong & Liu, Junfeng & Li, Bengang & Tao, Shu, 2016. "Modeling temporal variations in global residential energy consumption and pollutant emissions," Applied Energy, Elsevier, vol. 184(C), pages 820-829.
    13. Yang, Haiyue & Wang, Yazhou & Yu, Qianqian & Cao, Guoliang & Yang, Rue & Ke, Jiaona & Di, Xin & Liu, Feng & Zhang, Wenbo & Wang, Chengyu, 2018. "Composite phase change materials with good reversible thermochromic ability in delignified wood substrate for thermal energy storage," Applied Energy, Elsevier, vol. 212(C), pages 455-464.
    14. Ebrahim Morady & Madjid Soltani & Farshad Moradi Kashkooli & Masoud Ziabasharhagh & Armughan Al-Haq & Jatin Nathwani, 2022. "Improving Energy Efficiency by Utilizing Wetted Cellulose Pads in Passive Cooling Systems," Energies, MDPI, vol. 15(1), pages 1-17, January.
    15. Vega, A.M. & Santamaria, F. & Rivas, E., 2015. "Modeling for home electric energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 948-959.
    16. Colclough, Shane & McGrath, Teresa, 2015. "Net energy analysis of a solar combi system with Seasonal Thermal Energy Store," Applied Energy, Elsevier, vol. 147(C), pages 611-616.
    17. Hinker, Jonas & Hemkendreis, Christian & Drewing, Emily & März, Steven & Hidalgo Rodríguez, Diego I. & Myrzik, Johanna M.A., 2017. "A novel conceptual model facilitating the derivation of agent-based models for analyzing socio-technical optimality gaps in the energy domain," Energy, Elsevier, vol. 137(C), pages 1219-1230.
    18. Kütt, Lauri & Millar, John & Karttunen, Antti & Lehtonen, Matti & Karppinen, Maarit, 2018. "Thermoelectric applications for energy harvesting in domestic applications and micro-production units. Part I: Thermoelectric concepts, domestic boilers and biomass stoves," Renewable and Sustainable Energy Reviews, Elsevier, vol. 98(C), pages 519-544.
    19. Yan, Huaxia & Pan, Yan & Li, Zhao & Deng, Shiming, 2018. "Further development of a thermal comfort based fuzzy logic controller for a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 219(C), pages 312-324.
    20. Cui, Can & Zhang, Xin & Cai, Wenjian, 2020. "An energy-saving oriented air balancing method for demand controlled ventilation systems with branch and black-box model," Applied Energy, Elsevier, vol. 264(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6228-:d:451654. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.