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

Author

Listed:
  • 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)

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.

Suggested Citation

  • Vinícius Pereira Gonçalves & Andre Luiz Marques Serrano & Gabriel Arquelau Pimenta Rodrigues & Matheus Noschang de Oliveira & Rodolfo Ipolito Meneguette & Guilherme Dantas Bispo & Maria Gabriela Mendo, 2025. "An Empirically Validated Framework for Automated and Personalized Residential Energy-Management Integrating Large Language Models and the Internet of Energy," Energies, MDPI, vol. 18(14), pages 1-24, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:14:p:3744-:d:1701975
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    References listed on IDEAS

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    1. Faruqui, Ahmad & Sergici, Sanem & Sharif, Ahmed, 2010. "The impact of informational feedback on energy consumption—A survey of the experimental evidence," Energy, Elsevier, vol. 35(4), pages 1598-1608.
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    4. Zhou, Kaile & Fu, Chao & Yang, Shanlin, 2016. "Big data driven smart energy management: From big data to big insights," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 215-225.
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