Adaptive Energy Management for MATTER-Enabled Smart Homes

The proliferation of IoT devices in CSA MATTER-enabled smart homes presents new challenges and opportunities for intelligent energy management. This paper proposes a unified machine learning (ML) framework for dynamic energy optimization, integrating advanced prediction, anomaly detection, and reinforcement learning (RL) techniques. The energy demand prediction module employs a stacked ensemble learning model combining Random Forest Regression, Support Vector Regression, Gradient Boosting Machines, and AdaBoost with a meta-learner, achieving a prediction accuracy of 92%. For anomaly detection, a hybrid multi-stage approach incorporating Long Short-Term Memory (LSTM) networks, One-Class SVMs (OCSVM), and autoencoders delivers a precision of 0.97 and recall of 0.95 in identifying abnormal consumption patterns and potential cybersecurity threats. Resource allocation is optimized via RL strategies, including Q-learning, Deep Q-Networks (DQN), and actor-critic methods, achieving an Energy Efficiency Ratio (EER) of 0.82. The framework is evaluated across multiple metrics: energy consumption, detection accuracy, system resilience, and computational efficiency under edge-device constraints. A comprehensive analysis of hardware footprint, latency sensitivity, and data loss impact is presented, along with detailed anomaly detection criteria and threshold tuning to minimize false positives/negatives. The cybersecurity layer is validated through emulated attack scenarios, confirming robust threat detection. Additionally, interpretability of theMLmodels is addressed through the proposed future integration of Explainable AI (XAI) techniques such as SHAP and LIME, to foster end-user trust and transparency. This work demonstrates a scalable, adaptive, and secure ML-driven solution for energy management in MATTER-enabled smart homes, contributing significantly to sustainable and cost-efficient smart living environments.