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Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning

Author

Listed:
  • Seyed Morteza Moghimi

    (Department of Electrical and Computer Engineering, University of Victoria, STN CSC, P.O. Box 1700, Victoria, BC V8W 2Y2, Canada)

  • Thomas Aaron Gulliver

    (Department of Electrical and Computer Engineering, University of Victoria, STN CSC, P.O. Box 1700, Victoria, BC V8W 2Y2, Canada)

  • Ilamparithi Thirumarai Chelvan

    (Department of Electrical and Computer Engineering, University of Victoria, STN CSC, P.O. Box 1700, Victoria, BC V8W 2Y2, Canada
    These authors contributed equally to this work.)

  • Hossen Teimoorinia

    (Department of Physics and Astronomy, University of Victoria, Victoria, BC V8P 5C2, Canada
    NRC Herzberg Astronomy and Astrophysics, 5071 West Saanich Road, Victoria, BC V9E 2E7, Canada
    These authors contributed equally to this work.)

Abstract

This paper presents an occupant-centric load optimization framework for Smart Green Townhouses (SGTs). A hybrid Long Short-Term Memory and Convolutional Neural Network (LSTM-CNN) model is combined with real-time Internet of Things (IoT) data to predict and optimize energy usage based on occupant behavior and environmental conditions. Multi-Objective Particle Swarm Optimization (MOPSO) is applied to balance energy efficiency, cost reduction, and occupant comfort. This approach enables intelligent control of HVAC systems, lighting, and appliances. The proposed framework is shown to significantly reduce load demand, peak consumption, costs, and carbon emissions while improving thermal comfort and lighting adequacy. These results highlight the potential to provide adaptive solutions for sustainable residential energy management.

Suggested Citation

  • Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2025. "Occupant-Centric Load Optimization in Smart Green Townhouses Using Machine Learning," Energies, MDPI, vol. 18(13), pages 1-15, June.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3320-:d:1686498
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    References listed on IDEAS

    as
    1. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumarai Chelvan & Hossen Teimoorinia, 2024. "Load Optimization for Connected Modern Buildings Using Deep Hybrid Machine Learning in Island Mode," Energies, MDPI, vol. 17(24), pages 1-25, December.
    2. Katarina Bäcklund & Marco Molinari & Per Lundqvist & Björn Palm, 2023. "Building Occupants, Their Behavior and the Resulting Impact on Energy Use in Campus Buildings: A Literature Review with Focus on Smart Building Systems," Energies, MDPI, vol. 16(17), pages 1-21, August.
    3. César Benavente-Peces, 2019. "On the Energy Efficiency in the Next Generation of Smart Buildings—Supporting Technologies and Techniques," Energies, MDPI, vol. 12(22), pages 1-25, November.
    4. Seyed Morteza Moghimi & Thomas Aaron Gulliver & Ilamparithi Thirumai Chelvan, 2024. "Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning—A Review," Energies, MDPI, vol. 17(3), pages 1-20, January.
    5. Jangsten, Maria & Lindholm, Torbjörn & Dalenbäck, Jan-Olof, 2020. "Analysis of operational data from a district cooling system and its connected buildings," Energy, Elsevier, vol. 203(C).
    6. Abou Houran, Mohamad & Salman Bukhari, Syed M. & Zafar, Muhammad Hamza & Mansoor, Majad & Chen, Wenjie, 2023. "COA-CNN-LSTM: Coati optimization algorithm-based hybrid deep learning model for PV/wind power forecasting in smart grid applications," Applied Energy, Elsevier, vol. 349(C).
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