Estudo Bibliométrico de Gêmeos Digitais no Setor Energético Solar

Bibliometric Study of Digital Twins in the Solar Energy Sector

Autores

  • Lucas Emanuel Almeida Barboza Universidade Federal de Pernambuco-UFPE Autor

DOI:

https://doi.org/10.51473/rcmos.v1i2.2025.1777

Palavras-chave:

Gêmeos Digitais, Bibliometria, Otimização

Resumo

Os Gêmeos Digitais (GD) representam uma tecnologia emergente capaz de emular sistemas reais em ambientes virtuais, oferecendo soluções promissoras para o setor energético. Este trabalho apresenta uma análise bibliométrica sobre a aplicação de Gêmeos Digitais no setor de energia solar, visando identificar tendências, lacunas e principais tópicos de pesquisa. A metodologia baseou-se na coleta de dados nas bases Web of Science e Scopus, abrangendo o período de 2009 a 2024, seguida pelo processamento de correlação de termos através do software VOSviewer. Os resultados indicam que o tema é recente e em crescimento, com foco predominante no gerenciamento de sistemas, otimização de performance e uso intensivo de Machine Learning para previsão de recursos solares e potência. Conclui-se que a pesquisa na área está intrinsecamente ligada à modernização das usinas e eficiência operacional, apontando para um vasto campo de investigações futuras. 

Downloads

Os dados de download ainda não estão disponíveis.

Referências

ARTETXE, E.; URALDE, J.; BARAMBONES, O.; CALVO, I.; MARTIN, I. Maximum Power Point Tracker Controller for Solar Photovoltaic Based on Reinforcement Learning Agent with a Digital Twin. Mathematics, v. 11, 2023. DOI: 10.3390/math11092166. DOI: https://doi.org/10.3390/math11092166

BROADUS, R. N. Toward a definition of “bibliometrics.” Scientometrics, v. 12, p. 373–379, 1987. DOI: 10.1007/BF02016680. DOI: https://doi.org/10.1007/BF02016680

CHENG, T.; ZHU, X.; YANG, F.; WANG, W. Machine learning enabled learning-based optimization algorithm in digital twin simulator for management of smart islanded solar-based microgrids. Solar Energy, v. 250, p. 241–247, 2023. DOI: 10.1016/j.solener.2022.12.040. DOI: https://doi.org/10.1016/j.solener.2022.12.040

GHENAI, C.; HUSEIN, L. A.; AL NAHLAWI, M.; HAMID, A. K.; BETTAYEB, M. Recent trends of digital twin technologies in the energy sector: A comprehensive review. Sustainable Energy Technologies and Assessments, v. 54, 2022. DOI: 10.1016/j.seta.2022.102837. DOI: https://doi.org/10.1016/j.seta.2022.102837

GUI, Y. et al. Automatic voltage regulation application for PV inverters in low-voltage distribution grids – A digital twin approach. International Journal of Electrical Power & Energy Systems, v. 149, 109022, 2023. DOI: 10.1016/j.ijepes.2023.109022. DOI: https://doi.org/10.1016/j.ijepes.2023.109022

GUO, Z. et al. Intelligent Digital Twin Modelling for Hybrid PV-SOFC Power Generation System. Energies, v. 16, 2023. DOI: 10.3390/en16062806. DOI: https://doi.org/10.3390/en16062806

HONG, Y. Y.; PULA, R. A. Diagnosis of PV faults using digital twin and convolutional mixer with LoRa notification system. Energy Reports, v. 9, p. 1963–1976, 2023. DOI: 10.1016/j.egyr.2023.01.011. DOI: https://doi.org/10.1016/j.egyr.2023.01.011

HUANG, J.; KOROTEEV, D. D.; RYNKOVSKAYA, M. Machine learning-based demand response in PV-based smart home considering energy management in digital twin. Solar Energy, v. 252, p. 8–19, 2023. DOI: 10.1016/j.solener.2023.01.044. DOI: https://doi.org/10.1016/j.solener.2023.01.044

KHALED, N.; PATTEL, B.; SIDDIQUI, A. Digital twin model creation of solar panels. In: Digital Twin Development and Deployment on the Cloud. Elsevier, 2020. p. 137–162. DOI: 10.1016/b978-0-12-821631-6.00006-2. DOI: https://doi.org/10.1016/B978-0-12-821631-6.00006-2

LI, Y.; TAO, Q.; GONG, Y. Digital twin simulation for integration of blockchain and internet of things for optimal smart management of PV-based connected microgrids. Solar Energy, v. 251, p. 306–314, 2023. DOI: 10.1016/j.solener.2023.01.013. DOI: https://doi.org/10.1016/j.solener.2023.01.013

MINGERS, J.; LEYDESDORFF, L. A review of theory and practice in scientometrics. European Journal of Operational Research, v. 246, p. 1–19, 2015. DOI: 10.1016/j.ejor.2015.04.002. DOI: https://doi.org/10.1016/j.ejor.2015.04.002

NATGUNANATHAN, I. et al. Deakin Microgrid Digital Twin and Analysis of AI Models for Power Generation Prediction. Energy Conversion and Management: X, 100370, 2023. DOI: 10.1016/j.ecmx.2023.100370. DOI: https://doi.org/10.1016/j.ecmx.2023.100370

RAZO, D. E. G. et al. A genetic algorithm approach as a self-learning and optimization tool for PV power simulation and digital twinning. Energies, v. 13, 2020. DOI: 10.3390/en13246712. DOI: https://doi.org/10.3390/en13246712

SEHRAWAT, N.; VASHISHT, S.; SINGH, A. Solar irradiance forecasting models using machine learning techniques and digital twin: A case study with comparison. International Journal of Intelligent Networks, v. 4, p. 90–102, 2023. DOI: 10.1016/j.ijin.2023.04.001. DOI: https://doi.org/10.1016/j.ijin.2023.04.001

SEMERARO, C.; LEZOCHE, M.; PANETTO, H.; DASSISTI, M. Digital twin paradigm: A systematic literature review. Computers in Industry, v. 130, 2021. DOI: 10.1016/j.compind.2021.103469. DOI: https://doi.org/10.1016/j.compind.2021.103469

SLEITI, A. K.; KAPAT, J. S.; VESELY, L. Digital twin in energy industry: Proposed robust digital twin for power plant and other complex capital-intensive large engineering systems. Energy Reports, v. 8, p. 3704–3726, 2022. DOI: 10.1016/j.egyr.2022.02.305. DOI: https://doi.org/10.1016/j.egyr.2022.02.305

TAO, F.; XIAO, B.; QI, Q.; CHENG, J.; JI, P. Digital twin modeling. Journal of Manufacturing Systems, v. 64, p. 372–389, 2022. DOI: 10.1016/j.jmsy.2022.06.015. DOI: https://doi.org/10.1016/j.jmsy.2022.06.015

TUOMIRANTA, A. et al. Auto-Parametrizing the Digital Twin of Photovoltaic Power Systems. In: 38th European Photovoltaic Solar Energy Conference and Exhibition, 2021. p. 1022–1027. DOI: 10.4229/EUPVSEC20212021-5DO.1.4.

ULLAH, S. M. S. et al. Techno-economic impacts of Volt-VAR control on the high penetration of solar PV interconnection. Cleaner Energy Systems, v. 5, 100067, 2023. DOI: 10.1016/j.cles.2023.100067. DOI: https://doi.org/10.1016/j.cles.2023.100067

YAO, J. F. et al. Systematic review of digital twin technology and applications. Visual Computing for Industry, Biomedicine, and Art, v. 6, 2023. DOI: 10.1186/s42492-023-00137-4. DOI: https://doi.org/10.1186/s42492-023-00137-4

YOU, M. et al. Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties. Applied Energy, v. 305, 2022. DOI: 10.1016/j.apenergy.2021.117899. DOI: https://doi.org/10.1016/j.apenergy.2021.117899

YUAN, G.; XIE, F. Digital Twin-Based economic assessment of solar energy in smart microgrids using reinforcement learning technique. Solar Energy, v. 250, p. 398–408, 2023. DOI: 10.1016/j.solener.2022.12.031. DOI: https://doi.org/10.1016/j.solener.2022.12.031

Publicado

02.12.2025

Como Citar

BARBOZA, Lucas Emanuel Almeida. Estudo Bibliométrico de Gêmeos Digitais no Setor Energético Solar: Bibliometric Study of Digital Twins in the Solar Energy Sector. RCMOS - Revista Científica Multidisciplinar O Saber, Brasil, v. 1, n. 2, 2025. DOI: 10.51473/rcmos.v1i2.2025.1777. Disponível em: https://submissoesrevistarcmos.com.br/rcmos/article/view/1777. Acesso em: 4 dez. 2025.