A Machine Learning–Driven Framework for Predictive Energy Optimization and Intelligent Appliance Management in Smart Home Environments
Keywords:
Smart homes, Machine learning, Energy optimization, Predictive modelingAbstract
The rapid proliferation of smart home technologies and Internet of Things (IoT) devices has led to an unprecedented increase in residential energy consumption, necessitating intelligent and adaptive energy management solutions. This study proposes a comprehensive machine learning–driven framework for predictive energy optimization and intelligent appliance management in smart home environments. The framework integrates data-driven forecasting, appliance-level analytics, and adaptive optimization strategies to enhance energy efficiency while maintaining user comfort. Drawing upon existing research in machine learning, ensemble modeling, and reinforcement learning, the study develops a multi-layered architecture that leverages predictive analytics for real-time decision-making. The methodology incorporates hybrid machine learning models, including deep recurrent neural networks and ensemble techniques, to forecast energy consumption patterns and optimize appliance scheduling. Empirical evaluation demonstrates significant improvements in energy efficiency, peak load reduction, and cost optimization. The findings highlight the effectiveness of predictive modeling in enabling proactive energy management and reveal key trade-offs between computational complexity and system adaptability. The proposed framework contributes to the advancement of intelligent energy systems by bridging the gap between predictive analytics and real-time appliance control. The study concludes by identifying future research directions, including integration with renewable energy systems and explainable AI for enhanced transparency.
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Copyright (c) 2026 Dr. Yoga Saputra, Dr. Rini Oktaviani (Author)
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