MELISSA: An LLM-Powered Smart Home Energy Consumption Monitoring Framework - Rodrigues, Gabriel Arquelau Pimenta; Oliveira, Matheus Noschang de; Serrano, André Luiz Marques; Rocha Filho, Geraldo Pereira; Vergara, Guilherme Fay; Mosquéra, Letícia Rezende; Gonçalves, Vinícius Pereira (Anais do Simpósio Brasileiro de Computação Ubíqua e Pervasiva (SBCUP); 2025: Anais do XVII Simpósio Brasileiro de Computação Ubíqua e Pervasiva, 2025)
This work introduces MELISSA, a multi-agent system that uses Large Language Models (LLMs) to optimize household energy consumption by integrating historical analysis and meteorological inputs, acting as an intelligent Home Energy Management System (HEMS) for smart spaces. Through a twostage process that condenses data by approximately 99%, the system identifies consumption patterns and anomalies. The Gunning Fog Index indicates that the outputs are easily readable by the target audience, with a moderate Self-BLEU score. Thus, MELISSA offers an effective residential energy management solution, using LLMs to communicate with the end-users. Future enhancements include integrating energy generation data.