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Enabling Optimal Energy Management with Minimal IoT Requirements: A Legacy A/C Case Study

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  • Panagiotis Michailidis

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

  • Paschalis Pelitaris

    (Electrical and Computer Engineering Department, Polytechnic School of Xanthi, Democritus University of Thrace, 67100 Xanthi, Greece)

  • Christos Korkas

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

  • Iakovos Michailidis

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

  • Simone Baldi

    (School of Mathematics, Jiulonghu Campus, Southeast University, Nanjijng 211189, China
    Center for Systems and Control, Delft University of Technology, 2628 CD Delft, The Netherlands)

  • Elias Kosmatopoulos

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

Abstract

The existing literature on energy saving focuses on large-scale buildings, wherein the energy-saving potential is substantially larger than smaller-scale buildings. However, the research intensity is significantly less for small-scale deployments and their capacities to regulate energy use individually, directly and without depreciating users’ comfort and needs. The current research effort focused on energy saving and user satisfaction, concerning a low-cost—yet technically sophisticated—methodology for controlling conventional residential HVAC units through cheap yet reliable actuation and sensing and auxiliary IoT equipment. The basic ingredients of the proposed experimental methodology involve a conventional A/C unit, an Arduino microcontroller, typical wireless IoT sensors and actuators, a configured graphical environment and a sophisticated, model-free, optimization-and-control algorithm (PCAO) that portrays the ground basis for achieving improved performance results in comparison with conventional methods. The main goal of this study was to produce a system that would adequately and expeditiously achieve energy savings by utilizing minimal hardware/equipment (affordability). The system was designed to be easily expandable in terms of new units or thermal equipment (expandability) and also to be autonomous, requiring zero user interventions at the experimental site (automation). The real-life measurements were collected over two different seasonal periods of the year (winter, summer) and concerned a conventional apartment in the city of Xanthi, Northern Greece, where summers and winters exhibit quite diverse climate characteristics. The final results revealed the increased efficiency of PCAO’s optimization in comparison with a conventional rule-based control strategy (RBC), as concerns energy savings and user satisfaction.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:23:p:7910-:d:687606
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    References listed on IDEAS

    as
    1. Baldi, Simone & Michailidis, Iakovos & Ravanis, Christos & Kosmatopoulos, Elias B., 2015. "Model-based and model-free “plug-and-play” building energy efficient control," Applied Energy, Elsevier, vol. 154(C), pages 829-841.
    2. Antonopoulos, Ioannis & Robu, Valentin & Couraud, Benoit & Kirli, Desen & Norbu, Sonam & Kiprakis, Aristides & Flynn, David & Elizondo-Gonzalez, Sergio & Wattam, Steve, 2020. "Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 130(C).
    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. 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.
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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. 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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