Artificial Intelligence for the Optimization of Electrical Grids in Latin America
DOI:
https://doi.org/10.70577/213ara83Keywords:
Artificial intelligence, machine learning, models, optimization, networksAbstract
This article explores the transformation of electricity grids in Latin America through the integration of artificial intelligence (AI). With energy demand expected to triple by 2050, AI is crucial for optimizing efficiency, reliability, and the integration of renewable energy. Countries such as Brazil, Mexico, and Chile are leading this adoption, using AI to manage distribution, balance supply and demand, and improve grid reliability. The study highlights that linear regression models predict energy efficiency with high accuracy (R2 = 0.86), influenced by consumption, generation, and weather conditions. Optimization classification models achieve an accuracy close to 100%, while risk classification shows mixed results, with difficulties in minority classes, suggesting the need for data balancing. K-Means clustering identified three geographic segments of the grid with distinct operational and maintenance characteristics. ARIMA and LSTM models demonstrate a robust ability to predict energy demand and consumption, capturing complex temporal patterns. Linear optimization demonstrated effective balancing of energy distribution across diverse sources, and identified the potential for heuristic algorithms for future improvements. Despite challenges such as class imbalance in risk data, the need for more robust fault prediction models, and dynamic data integration, AI offers a promising path toward more efficient, resilient grids with greater customer satisfaction.
References
[1] Mukhamediev, R. I., Popova, Y., Kuchin, Y., Zaitseva, E., Kalimoldayev, A., Symagulov, A., ... & Yelis, M. (2022). Review of artificial intelligence and machine learning technologies: Classification, restrictions, opportunities and challenges. Mathematics, 10(15), 2552. https://doi.org/10.3390/math10152552
[2] Suman, A. (2021). Role of renewable energy technologies in climate change adaptation and mitigation: A brief review from Nepal. Renewable and Sustainable Energy Reviews, 151, 111524. https://doi.org/10.1016/j.rser.2021.111524
[3] Beltrán Gallego, J. D., Quintero Ríos, M., López García, D., & Carvajal Quintero, S. X. (2022). Energy Management Systems in Latin American Industry: Case Study Colombia. TecnoLógicas, 25(54). http://dx.doi.org/10.22430/22565337.2379
[4] Greenstein, S. (2022). Preserving the rule of law in the era of artificial intelligence (AI). Artificial Intelligence and Law, 30(3), 291-323. https://link.springer.com/article/10.1007/s10506-021-09294-4
[5] Entezari, A., Aslani, A., Zahedi, R., & Noorollahi, Y. (2023). Artificial intelligence and machine learning in energy systems: A bibliographic perspective. Energy Strategy Reviews, 45, 101017. https://doi.org/10.1016/j.esr.2022.101017
[6] Naik, N., Hameed, B. M., Shetty, D. K., Swain, D., Shah, M., Paul, R., ... & Somani, B. K. (2022). Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility?. Frontiers in surgery, 9, 862322. http://dx.doi.org/10.3389/fsurg.2022.862322
[7] Gallegos, J., Arévalo, P., Montaleza, C., & Jurado, F. (2024). Sustainable electrification—advances and challenges in electrical-distribution networks: a review. Sustainability, 16(2), 698. https://doi.org/10.3390/su16020698
[8] Mbungu, N. T., Ismail, A. A., AlShabi, M., Bansal, R. C., Elnady, A., & Hamid, A. K. (2023). Control and estimation techniques applied to smart microgrids: A review. Renewable and Sustainable Energy Reviews, 179, 113251. https://doi.org/10.1016/j.rser.2023.113251
[9] Liu, Z., Gao, Y., & Liu, B. (2022). An artificial intelligence-based electric multiple units using a smart power grid system. Energy Reports, 8, 13376-13388. https://doi.org/10.1016/j.egyr.2022.09.138
[10] Kong, L., Tan, J., Huang, J., Chen, G., Wang, S., Jin, X., ... & Das, S. K. (2022). Edge-computing-driven internet of things: A survey. ACM Computing Surveys, 55(8), 1-41. http://dx.doi.org/10.1109/JIOT.2022.3200431
[11] Lu, S., Lu, J., An, K., Wang, X., & He, Q. (2023). Edge computing on IoT for machine signal processing and fault diagnosis: A review. IEEE Internet of Things Journal, 10(13), 11093-11116. https://ieeexplore.ieee.org/document/10026418
[12] Gehrke, P., Goretti, A. A. T., & Avila, L. V. (2021). Impactos da matriz energética no desenvolvimento sustentável do Brasil. Revista de Administração da UFSM, 14, 1032-1049. http://dx.doi.org/10.5902/1983465964409
[13] Bhattarai, T. N., Ghimire, S., Mainali, B., Gorjian, S., Treichel, H., & Paudel, S. R. (2023). Applications of smart grid technology in Nepal: status, challenges, and opportunities. Environmental Science and Pollution Research, 30(10), 25452-25476. https://doi.org/10.1007/s11356-022-19084-3
[14] Mazhar, T., Irfan, H. M., Haq, I., Ullah, I., Ashraf, M., Shloul, T. A., ... & Elkamchouchi, D. H. (2023). Analysis of challenges and solutions of IoT in smart grids using AI and machine learning techniques: A review. Electronics, 12(1), 242. https://doi.org/10.3390/electronics12010242
[15] Icaza-Alvarez, D., Jurado, F., & Tostado-Véliz, M. (2024). Smart energy planning for the decarbonization of Latin America and the Caribbean in 2050. Energy Reports, 11, 6160-6185. https://doi.org/10.1016/j.egyr.2024.05.067
[16] Lage, M., & Castro, R. (2022). A practical review of the public policies used to promote the implementation of pv technology in smart grids: the case of portugal. Energies, 15(10), 3567. http://dx.doi.org/10.3390/en15103567
[17] Guo, C., Luo, F., Cai, Z., & Dong, Z. Y. (2021). Integrated energy systems of data centers and smart grids: State-of-the-art and future opportunities. Applied Energy, 301, 117474. https://doi.org/10.1016/j.apenergy.2021.117474
[18] Niet, I. A., Dekker, R., & van Est, R. (2022). Seeking public values of digital energy platforms. Science, Technology, & Human Values, 47(3), 380-403. http://dx.doi.org/10.1177/01622439211054430
[19] Li, Y., & Yan, J. (2022). Cybersecurity of smart inverters in the smart grid: A survey. IEEE Transactions on Power Electronics, 38(2), 2364-2383. https://ieeexplore.ieee.org/document/9889215
[20] Mohammadi, F. (2021). Emerging challenges in smart grid cybersecurity enhancement: A review. Energies, 14(5), 1380. https://doi.org/10.3390/en14051380
[21] Kumar, P., Chauhan, S., & Awasthi, L. K. (2023). Artificial intelligence in healthcare: review, ethics, trust challenges & future research directions. Engineering Applications of Artificial Intelligence, 120, 105894. https://doi.org/10.1016/j.engappai.2023.105894
[22] Pimenow, S., Pimenowa, O., & Prus, P. (2024). Challenges of artificial intelligence development in the context of energy consumption and impact on climate change. Energies, 17(23), 5965. https://doi.org/10.3390/en17235965
[23] Zhang, Y., Teoh, B. K., Wu, M., Chen, J., & Zhang, L. (2023). Data-driven estimation of building energy consumption and GHG emissions using explainable artificial intelligence. Energy, 262, 1 https://doi.org/10.1016/j.energy.2022.125468
[24] Fernández-Miranda, M., Román-Acosta, D., Jurado-Rosas, A. A., Limón-Dominguez, D., & Torres-Fernández, C. (2024). Artificial intelligence in Latin American universities: Emerging challenges. Computación y Sistemas, 28(2), 435-450. https://doi.org/10.13053/cys-28-2-4822
[25] Sahani, N., Zhu, R., Cho, J. H., & Liu, C. C. (2023). Machine learning-based intrusion detection for smart grid computing: A survey. ACM Transactions on Cyber-Physical Systems, 7(2), 1-31. http://dx.doi.org/10.1145/3578366
[26] Bashir, A. K., Khan, S., Prabadevi, B., Deepa, N., Alnumay, W. S., Gadekallu, T. R., & Maddikunta, P. K. R. (2021). Comparative analysis of machine learning algorithms for prediction of smart grid stability. International Transactions on Electrical Energy Systems, 31(9), e12706. http://dx.doi.org/10.1002/2050-7038.12706
[27] Mostafa, N., Ramadan, H. S. M., & Elfarouk, O. (2022). Renewable energy management in smart grids by using big data analytics and machine learning. Machine Learning with Applications, 9, 100363. https://doi.org/10.1016/j.mlwa.2022.100363
[28] Yan, Z., & Wen, H. (2021). Performance analysis of electricity theft detection for the smart grid: An overview. IEEE Transactions on Instrumentation and Measurement, 71, 1-28. http://dx.doi.org/10.1109/TIM.2021.3127649
[29] Geng, Y., Zhang, N., & Zhu, R. (2023). Research progress analysis of sustainable smart grid based on CiteSpace. Energy Strategy Reviews, 48, 101111. https://doi.org/10.1016/j.esr.2023.101111
[30] Xia, W., Jiang, Y., Chen, X., & Zhao, R. (2022). Application of machine learning algorithms in municipal solid waste management: A mini review. Waste Management & Research, 40(6), 609-624. http://dx.doi.org/10.1177/0734242X211033716
[31] Khalid, M. (2024). Smart grids and renewable energy systems: Perspectives and grid integration challenges. Energy Strategy Reviews, 51, 101299. https://doi.org/10.1016/j.esr.2024.101299
[32] Omitaomu, O. A., & Niu, H. (2021). Artificial intelligence techniques in smart grid: A survey. Smart Cities, 4(2), 548-568. https://doi.org/10.3390/smartcities4020029
[33] Lamnatou, C., Chemisana, D., & Cristofari, C. (2022). Smart grids and smart technologies in relation to photovoltaics, storage systems, buildings and the environment. Renewable Energy, 185, 1376-1391. https://doi.org/10.1016/j.renene.2021.11.019
[34] Berdugo-Sarmiento, K., Silva-Ortega, J., & Eras, J. J. C. (2024). Electricity Public Service Quality Management in Colombia. In 2024 9th International Engineering, Sciences and Technology Conference (IESTEC) (pp. 587-592). IEEE. http://dx.doi.org/10.1109/IESTEC62784.2024.10820198
[35] Mohiddin, M. K., Kohli, R., Dutt, V. S. I., Dixit, P., & Michal, G. (2021). Energy‐Efficient Enhancement for the Prediction‐Based Scheduling Algorithm for the Improvement of Network Lifetime in WSNs. Wireless Communications and Mobile Computing, 2021(1), 9601078. http://dx.doi.org/10.1155/2021/9601078
[36] Aghsaee, R., Hecht, C., Schwinger, F., Figgener, J., Jarke, M., & Sauer, D. U. (2023). Data-driven, short-term prediction of charging station occupation. Electricity, 4(2), 134-153. https://doi.org/10.3390/electricity4020009
[37] Shern, S. J., Sarker, M. T., Haram, M. H. S. M., Ramasamy, G., Thiagarajah, S. P., & Al Farid, F. (2024). Artificial Intelligence Optimization for User Prediction and Efficient Energy Distribution in Electric Vehicle Smart Charging Systems. Energies, 17(22), 5772. https://doi.org/10.3390/en17225772
[38] Kaur, R., Gabrijelčič, D., & Klobučar, T. (2023). Artificial intelligence for cybersecurity: Literature review and future research directions. Information Fusion, 97, 101804. https://doi.org/10.1016/j.inffus.2023.101804
[39] Paret, P., Finegan, D., & Narumanchi, S. (2023). Artificial intelligence for power electronics in electric vehicles: challenges and opportunities. Journal of Electronic Packaging, 145(3), 034501. https://doi.org/10.1115/1.4056306
[40] Arévalo, P., Ochoa-Correa, D., & Villa-Ávila, E. (2024). A systematic review on the integration of artificial intelligence into energy management systems for electric vehicles: Recent advances and future perspectives. World Electric Vehicle Journal, 15(8), 364. https://doi.org/10.3390/wevj15080364
[41] Mhlanga, D. (2023). Artificial intelligence and machine learning for energy consumption and production in emerging markets: a review. Energies, 16(2), 745. https://doi.org/10.3390/en16020745
[42] Wang, Q., Zhang, F., Li, R., & Sun, J. (2024). Does artificial intelligence promote energy transition and curb carbon emissions? The role of trade openness. Journal of Cleaner Production, 447, 141298. https://doi.org/10.1016/j.jclepro.2024.141298
[43] Yao, Z., Lum, Y., Johnston, A., Mejia-Mendoza, L. M., Zhou, X., Wen, Y., ... & Seh, Z. W. (2023). Machine learning for a sustainable energy future. Nature Reviews Materials, 8(3), 202-215. https://doi.org/10.1038/s41578-022-00490-5
[44] Liu, Y., Chen, H., Zhang, L., & Feng, Z. (2021). Enhancing building energy efficiency using a random forest model: A hybrid prediction approach. Energy Reports, 7, 5003-5012. https://doi.org/10.1016/j.egyr.2021.07.135
45] Nie, P., Roccotelli, M., Fanti, M. P., Ming, Z., & Li, Z. (2021). Prediction of home energy consumption based on gradient boosting regression tree. Energy Reports, 7, 1246-1255. https://doi.org/10.1016/j.egyr.2021.02.006
[46] Miraftabzadeh, S. M., Colombo, C. G., Longo, M., & Foiadelli, F. (2023). K-means and alternative clustering methods in modern power systems. Ieee Access, 11, 119596-119633. http://dx.doi.org/10.1109/ACCESS.2023.3327640
[47] Pierre, A. A., Akim, S. A., Semenyo, A. K., & Babiga, B. (2023). Peak electrical energy consumption prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU approaches. Energies, 16(12), 4739. https://doi.org/10.3390/en16124739
[48] Prattico, D., Laganá, F., Oliva, G., Fiorillo, A. S., Pullano, S. A., Calcagno, S., ... & La Foresta, F. (2024, July). Sensors and Integrated Electronic Circuits for Monitoring Machinery On Wastewater Treatment: Artificial Intelligence Approach. In 2024 IEEE Sensors Applications Symposium (SAS) (pp. 1-6).
IEEE. https://scholar.google.com/citations?user=ccruDcsAAAAJ&hl=it
Downloads
Published
Issue
Section
License
Copyright (c) 2025 Innovación Integral
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
Eres libre de:
- Compartir : copiar y redistribuir el material en cualquier medio o formato
- Adaptar : remezclar, transformar y desarrollar el material
- El licenciante no puede revocar estas libertades siempre y cuando usted cumpla con los términos de la licencia.
En los siguientes términos:
-
- Atribución : Debe otorgar el crédito correspondiente , proporcionar un enlace a la licencia e indicar si se realizaron cambios . Puede hacerlo de cualquier manera razonable, pero no de ninguna manera que sugiera que el licenciante lo respalda a usted o a su uso.
- No comercial : no puede utilizar el material con fines comerciales .
- CompartirIgual — Si remezcla, transforma o construye sobre el material, debe distribuir sus contribuciones bajo la misma licencia que el original.
- Sin restricciones adicionales : no puede aplicar términos legales ni medidas tecnológicas que restrinjan legalmente a otros hacer algo que la licencia permite.
