Edge-based personalized energy optimization using smart plug with micro-moments driven recommender system

Reducing residential energy consumption requires not only intelligent monitoring but also timely, personalized user engagement to drive meaningful behavioral change. However, many existing systems rely on centralized data processing, raising privacy concerns, increasing communication overhead, and limiting real-time responsiveness. They also often lack context-aware feedback mechanisms that help users make informed decisions. This study introduces a fully integrated smart plug system that combines micro-moment (MM) classification with a lightweight recommender system (RS) to deliver privacy-preserving, real-time energy-saving suggestions. Built on a semi-supervised federated learning (SSFL) framework, the system enables on-device inference without transmitting raw user data ensuring data privacy while supporting adaptive behavior modeling. The RS is triggered by transitions in MM classes and delivers recommendations enriched with real-time energy metrics, estimated cost savings, and environmental context such as room temperature and air quality. The system was implemented using Home Assistant (HA) for seamless sensor integration and appliance control and deployed in a 14-day pilot study across six residential households in Doha. During the deployment, the system achieved an average energy reduction of approximately 15.63% of total usage, with a 75% acceptance rate for anomaly-triggered recommendations. These results demonstrate the practicality and effectiveness of decentralized, edge-based intelligence for energy behavior modeling and user guidance. Overall, the proposed smart plug system shows strong promise for secure, responsive, and user-centric energy optimization in real-world smart home environments.