IoT-Based Household Energy Monitoring with Preliminary Large Language Model-Assisted Recommendations

Abstract

IoT-based household energy monitoring systems are effective for real-time data acquisition but often provide limited interpretive support for non-technical users. Most existing implementations focus on measurement and visualization, with less attention to converting raw consumption data into actionable recommendations. This study presents a prototype household energy monitoring system that integrates an ESP32-based IoT platform, PZEM-004T sensors, Firebase Realtime Database, and a cloud-hosted Large Language Model (LLM) in a hybrid edge-cloud architecture. Sensor validation was performed by comparing PZEM-004T readings with clamp meter measurements. The results showed low overall error, with voltage measurement achieving an MAE of 0.505 V, RMSE of 0.678 V, and MAPE of 0.231%, while power measurement achieved an MAE of 0.206 W, RMSE of 0.267 W, and MAPE of 0.889%. The LLM module was assessed through scenario-based functional evaluation, where it generated contextual explanations, identified potential energy-use anomalies, and produced quantified energy-saving suggestions. A real-user evaluation involving 50 respondents further indicated generally positive perceptions of the generated recommendations in terms of clarity, relevance, usefulness, trust, and behavioral intention, although the findings remain limited to perceived user responses rather than long-term behavioral outcomes. Overall, the results demonstrate the feasibility of employing an LLM as an interpretive layer in IoT-based household energy monitoring and indicate its potential to improve the accessibility of energy information for non-technical users.