EnergiQ: A Prescriptive Large Language Model-Driven Intelligent Platform for Interpreting Appliance Energy...

Area of Science:

• Smart Home Technology
• Artificial Intelligence
• Energy Management Systems

Background:

• Smart sensors offer opportunities but also complexities in residential energy management.
• Current energy management systems (EMS) lack interpretability, adaptability, and user engagement.
• Bridging the gap between complex energy analytics and user comprehension is crucial.

Purpose of the Study:

• To present EnergiQ, an intelligent platform integrating sensors and LLMs for enhanced energy management.
• To improve user comprehension of energy consumption data through natural language feedback.
• To foster proactive consumer engagement in energy efficiency and sustainable home practices.

Main Methods:

• Integration of smart plug-based IoT sensing and time-series machine learning (ML).
• Utilization of XGBoost classifiers for appliance identification and CNN-LSTM autoencoders for anomaly detection.
• Implementation of an LLM reasoning layer with instruction-tuned models for personalized feedback.

Main Results:

• High appliance classification accuracy (94%) achieved with statistical feature-based XGBoost.
• Effective anomaly detection demonstrated across various devices using a CNN-LSTM autoencoder.
• LLM layer showed over 91% agreement with expert energy-saving recommendations.

Conclusions:

• EnergiQ successfully translates complex energy data into intuitive, actionable insights.
• The platform empowers consumers to proactively manage energy use and enhance efficiency.
• LLM integration significantly improves the interpretability and user engagement of EMS.