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

Seyed Morteza Moghimi (), Thomas Aaron Gulliver (), Ilamparithi Thirumarai Chelvan and Hossen Teimoorinia
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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
Hossen Teimoorinia: Department of Physics and Astronomy, University of Victoria, Victoria, BC V8P 5C2, Canada

Energies, 2025, vol. 18, issue 13, 1-16

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.

Keywords: occupant satisfaction; green townhouse; smart townhouse; machine learning; load demand; optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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