Transferência de Dados entre Agentes em um Sistema Multiagente no Domínio do Gerenciamento de Energia em Veículos Elétricos
Resumo
A gestão energética dos veículos elétricos compreende um conjunto de procedimentos e atividades cujo foco final é a otimização do uso de energia. Neste cenário, nos últimos anos, muitas pesquisas foram desenvolvidas para fornecer estruturas que auxiliem esses procedimentos e atividades através de técnicas de Aprendizado de Máquina e sistemas multiagentes. Porém, esses trabalhos não detalham o gerenciamento de dados envolvido nesses ambientes, nem mesmo como os esquemas de dados são estruturados, inclusive considerando a questão do sigilo dos dados sensíveis. Este artigo propõe um modelo de sistema multiagente e detalha a transferência e compartilhamento de dados entre os agentes envolvidos. Os testes realizados comprovam a eficiência do modelo adotado e a segurança na troca de dados entre agentes.
Referências
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Alqahtani, M., Scott, M. J., and Hu, M. (2022). Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning. Computers & Industrial Engineering, 169:108180.
ALSHAMMARI, S. T. and EASSA, F. (2018). Designing a Flexible Architecture based on mobile agents for Executing Query in Cloud Databases. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–6.
Bansal, A. K., Ramohanarao, K., and Rao, A. (1998). Distributed storage of replicated beliefs to facilitate recovery of distributed intelligent agents. In Intelligent Agents IV Agent Theories, Architectures, and Languages, pages 77–91, Berlin, Heidelberg. Springer.
BRASIL (2018). Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD).
Bryła, P., Chatterjee, S., and Ciabiada-Bryła, B. (2023). Consumer adoption of electric vehicles: A systematic literature review. Energies, 16(1).
Calearo, L., Marinelli, M., and Ziras, C. (2021). A review of data sources for electric vehicle integration studies. Renewable and Sustainable Energy Reviews, 151:111518.
Khan, M. N. A. (2019). Multi-agent Based Forensic Analysis Framework for Infrastructures Involving Storage Networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89(2):291–309.
Korovin, I. S., Ivanov, D. Y., and Semenistyi, S. A. (2020). The Algorithm of Distributed Database for Multi-Agent Interaction. In ACAI ’19, ACAI ’19, pages 303–307, New York, NY, USA. Association for Computing Machinery.
Nizami, M. S. H., Hossain, M. J., and Mahmud, K. (2021). A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks. IEEE Systems Journal, 15(2):2066–2077.
Palanca, J., Terrasa, A., Julian, V., and Carrascosa, C. (2020). Spade 3: Supporting the new generation of multi-agent systems. IEEE Access, 8:182537–182549.
Pirani, M., Bonci, A., and Longhi, S. (2022). Towards a formal model of computation for RMAS. In Longo, F., Affenzeller, M., and Padovano, A., editors, ISM 2021, volume 200 of Procedia Computer Science, pages 865–877. ELSEVIER SCIENCE BV.
Ramasubramian, P. and Kannan, A. (2004). Intelligent Multi-Agent based BackPropagation Neural Network forecasting model for statistical database anomaly prevention system. In ICISIP, pages 108–113. IEEE.
Saner, C. B., Trivedi, A., and Srinivasan, D. (2022). A Cooperative Hierarchical MultiAgent System for EV Charging Scheduling in Presence of Multiple Charging Stations. IEEE Transactions on Smart Grid, 13(3):2218–2233.
Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., and Nijim, M. (2020). Machine Learning Approaches for EV Charging Behavior: A Review. IEEE Access, 8:168980–168993.
Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., and Nijim, M. (2021). Prediction of ev charging behavior using machine learning. IEEE Access, 9:111576–111586.
Zhai, Z., Su, S., Liu, R., Yang, C., and Liu, C. (2019). Agent–cellular automata model for the dynamic fluctuation of ev traffic and charging demands based on machine learning algorithm. Neural Computing and Applications, 31:4639–4652.
Alqahtani, M., Scott, M. J., and Hu, M. (2022). Dynamic energy scheduling and routing of a large fleet of electric vehicles using multi-agent reinforcement learning. Computers & Industrial Engineering, 169:108180.
ALSHAMMARI, S. T. and EASSA, F. (2018). Designing a Flexible Architecture based on mobile agents for Executing Query in Cloud Databases. In 2018 21st Saudi Computer Society National Computer Conference (NCC), pages 1–6.
Bansal, A. K., Ramohanarao, K., and Rao, A. (1998). Distributed storage of replicated beliefs to facilitate recovery of distributed intelligent agents. In Intelligent Agents IV Agent Theories, Architectures, and Languages, pages 77–91, Berlin, Heidelberg. Springer.
BRASIL (2018). Lei nº 13.709, de 14 de agosto de 2018. Lei Geral de Proteção de Dados Pessoais (LGPD).
Bryła, P., Chatterjee, S., and Ciabiada-Bryła, B. (2023). Consumer adoption of electric vehicles: A systematic literature review. Energies, 16(1).
Calearo, L., Marinelli, M., and Ziras, C. (2021). A review of data sources for electric vehicle integration studies. Renewable and Sustainable Energy Reviews, 151:111518.
Khan, M. N. A. (2019). Multi-agent Based Forensic Analysis Framework for Infrastructures Involving Storage Networks. Proceedings of the National Academy of Sciences, India Section A: Physical Sciences, 89(2):291–309.
Korovin, I. S., Ivanov, D. Y., and Semenistyi, S. A. (2020). The Algorithm of Distributed Database for Multi-Agent Interaction. In ACAI ’19, ACAI ’19, pages 303–307, New York, NY, USA. Association for Computing Machinery.
Nizami, M. S. H., Hossain, M. J., and Mahmud, K. (2021). A Coordinated Electric Vehicle Management System for Grid-Support Services in Residential Networks. IEEE Systems Journal, 15(2):2066–2077.
Palanca, J., Terrasa, A., Julian, V., and Carrascosa, C. (2020). Spade 3: Supporting the new generation of multi-agent systems. IEEE Access, 8:182537–182549.
Pirani, M., Bonci, A., and Longhi, S. (2022). Towards a formal model of computation for RMAS. In Longo, F., Affenzeller, M., and Padovano, A., editors, ISM 2021, volume 200 of Procedia Computer Science, pages 865–877. ELSEVIER SCIENCE BV.
Ramasubramian, P. and Kannan, A. (2004). Intelligent Multi-Agent based BackPropagation Neural Network forecasting model for statistical database anomaly prevention system. In ICISIP, pages 108–113. IEEE.
Saner, C. B., Trivedi, A., and Srinivasan, D. (2022). A Cooperative Hierarchical MultiAgent System for EV Charging Scheduling in Presence of Multiple Charging Stations. IEEE Transactions on Smart Grid, 13(3):2218–2233.
Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., and Nijim, M. (2020). Machine Learning Approaches for EV Charging Behavior: A Review. IEEE Access, 8:168980–168993.
Shahriar, S., Al-Ali, A. R., Osman, A. H., Dhou, S., and Nijim, M. (2021). Prediction of ev charging behavior using machine learning. IEEE Access, 9:111576–111586.
Zhai, Z., Su, S., Liu, R., Yang, C., and Liu, C. (2019). Agent–cellular automata model for the dynamic fluctuation of ev traffic and charging demands based on machine learning algorithm. Neural Computing and Applications, 31:4639–4652.
Publicado
10/04/2024
Como Citar
VEIGA, Carlos E. da; MELLO, Ronaldo S.; RAMOS, Carlos; CORCHADO, Juan Manuel; DORNELES, Carina F..
Transferência de Dados entre Agentes em um Sistema Multiagente no Domínio do Gerenciamento de Energia em Veículos Elétricos. In: ESCOLA REGIONAL DE BANCO DE DADOS (ERBD), 19. , 2024, Farroupilha/RS.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2024
.
p. 1-10.
ISSN 2595-413X.
DOI: https://doi.org/10.5753/erbd.2024.238833.
