An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy

Vinícius Pereira Gonçalves (), Andre Luiz Marques Serrano (), Gabriel Arquelau Pimenta Rodrigues (), Matheus Noschang de Oliveira, Rodolfo Ipolito Meneguette, Guilherme Dantas Bispo, Maria Gabriela Mendonça Peixoto and Geraldo Pereira Rocha Filho
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Vinícius Pereira Gonçalves: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Andre Luiz Marques Serrano: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Gabriel Arquelau Pimenta Rodrigues: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Matheus Noschang de Oliveira: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Rodolfo Ipolito Meneguette: Institute of Mathematical and Computer Sciences, University of Sao Paulo, São Carlos 13566-590, Brazil
Guilherme Dantas Bispo: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Maria Gabriela Mendonça Peixoto: Department of Electrical Engineering, University of Brasilia, Federal District, Brasilia 70910-900, Brazil
Geraldo Pereira Rocha Filho: Department of Exact and Technological Sciences, State University of Southwest Bahia, Vitória da Conquista 45083-900, Brazil

Energies, 2025, vol. 18, issue 14, 1-24

Abstract: The growing global demand for energy has resulted in a demand for innovative strategies for residential energy management. This study explores a novel framework—MELISSA (Modern Energy LLM-IoE Smart Solution for Automation)—that integrates Internet of Things (IoT) sensor networks with Large Language Models (LLMs) to optimize household energy consumption through intelligent automation and personalized interactions. The system combines real-time monitoring, machine learning algorithms for behavioral analysis, and natural language processing to deliver personalized, actionable recommendations through a conversational interface. A 12-month randomized controlled trial was conducted with 100 households, which were stratified across four socioeconomic quintiles in metropolitan areas. The experimental design included the continuous collection of IoT data. Baseline energy consumption was measured and compared with post-intervention usage to assess system impact. Statistical analyses included k-means clustering, multiple linear regression, and paired t -tests. The system achieved its intended goal, with a statistically significant reduction of 5.66% in energy consumption (95% CI: 5.21–6.11%, p < 0.001 ) relative to baseline, alongside high user satisfaction (mean = 7.81, SD = 1.24). Clustering analysis ( k = 4 , silhouette = 0.68) revealed four distinct energy-consumption profiles. Multiple regression analysis ( R 2 = 0.68 , p < 0.001 ) identified household size, ambient temperature, and frequency of user engagement as the principal determinants of consumption. This research advances the theoretical understanding of human–AI interaction in energy management and provides robust empirical evidence of the effectiveness of LLM-mediated behavioral interventions. The findings underscore the potential of conversational AI applications in smart homes and have practical implications for optimization of residential energy use.

Keywords: internet of things (IoT); large language models (LLMs); residential energy management; smart energy solutions; energy consumption optimization; machine learning for energy efficiency; user-centered energy management; smart home automation; energy consumption patterns; personalized energy recommendations (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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