Modelo matemático para la predicción de consumo y generación de energía eléctrica por medio de grupos electrógenos que utilizan petróleo y diésel de la empresa Petroecuador
Palabras clave:
predicción energética, aprendizaje automático, grupos electrógenos, eficiencia energética, Petroecuador
Resumen
Este estudio desarrolla un modelo matemático para predecir el consumo y la generación de energía eléctrica en los grupos electrógenos de Petroecuador que utilizan petróleo y diésel. Mediante la implementación de técnicas de aprendizaje automático, específicamente árboles de decisión y modelos ARIMA, se logró capturar las relaciones entre el consumo de combustibles y la producción de energía. El análisis de datos históricos reveló una fuerte relación lineal entre el consumo de crudo y la generación de potencia, mientras que el diésel mostró una relación no lineal y más débil. El modelo alcanzó un coeficiente de determinación de 0.97 en la validación con datos reales, demostrando una capacidad predictiva excepcional. Esta precisión se logró utilizando datos diarios detallados, subrayando la importancia de mantener registros frecuentes y minuciosos. El modelo desarrollado no solo permite predicciones precisas, sino que también ofrece una herramienta versátil para la planificación estratégica, la optimización de inventarios, la programación de mantenimiento y la mejora de la eficiencia energética global de Petroecuador. Este estudio establece una base sólida para futuras investigaciones en el campo de la predicción energética y la gestión eficiente de recursos en la industria petrolera y energética.
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Gao, J., Chen, H., Li, Y., Chen, J., Zhang, Y., Dave, K., & Huang, Y. (2019). Fuel consumption and exhaust emissions of diesel vehicles in worldwide harmonized light vehicles test cycles and their sensitivities to eco-driving factors. Energy Conversion and Management, 196, 605–613. https://doi.org/10.1016/J.ENCONMAN.2019.06.038
Keith, T. Z. (2019). Multiple regression and beyond: An introduction to multiple regression and structural equation modeling. Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling, 1–639. https://doi.org/10.4324/9781315162348/MULTIPLE-REGRESSION-BEYOND-TIMOTHY-KEITH
Khairi, D. M., Abas, M. A., Muhamad, S. F., & Wan Salim, W. S.-I. (2021). View of Fuel consumption mathematical models for road vehicle – A review. Progress in Energy and Environment. https://akademiabaru.com/submit/index.php/progee/article/view/3392/2799
Krasnyuk, M., Hrashchenko, I. S., Goncharenko, S., & Krasniuk, S. (2022). Hybrid application of decision trees, fuzzy logic and production rules for supporting investment decision making (on the example of an oil and gas producing company). ACCESS Journal: Access to Science, Business, Innovation in Digital Economy, 3(3), 278–291. https://doi.org/10.46656/ACCESS.2022.3.3(7)
Markiewicz, M., & Muślewski, Ł. (2020). Survey performance and emission parameters of diesel engine powered by diesel oil and fatty acid methyl esters using fuzzy logic techniques. Fuel, 277, 118179. https://doi.org/10.1016/J.FUEL.2020.118179
Shih, S. Y., Sun, F. K., & Lee, H. yi. (2019). Temporal pattern attention for multivariate time series forecasting. Machine Learning, 108(8–9), 1421–1441. https://doi.org/10.1007/S10994-019-05815-0/TABLES/4
Van Ruijven, B. J., De Cian, E., & Sue Wing, I. (2019). Amplification of future energy demand growth due to climate change. Nature Communications 2019 10:1, 10(1), 1–12. https://doi.org/10.1038/s41467-019-10399-3
Veza, I., Afzal, A., Mujtaba, M. A., Tuan Hoang, A., Balasubramanian, D., Sekar, M., Fattah, I. M. R., Soudagar, M. E. M., EL-Seesy, A. I., Djamari, D. W., Hananto, A. L., Putra, N. R., & Tamaldin, N. (2022). Review of artificial neural networks for gasoline, diesel and homogeneous charge compression ignition engine. Alexandria Engineering Journal, 61(11), 8363–8391. https://doi.org/10.1016/J.AEJ.2022.01.072
Yusif Mahmoud Ahmed, J. (2023). Optimized Fuel Efficiency and Management of Diesel-Powered Thermal Plants for Power Generation Stations in Freetown. SSRN Electronic Journal. https://doi.org/10.2139/SSRN.4401128
Zhang, Y., Vand, B., & Baldi, S. (2022). A Review of Mathematical Models of Building Physics and Energy Technologies for Environmentally Friendly Integrated Energy Management Systems. Buildings 2022, Vol. 12, Page 238, 12(2), 238. https://doi.org/10.3390/BUILDINGS12020238
X. Han and G. Hou, "Application of Predictive Algorithms in Informational Learning," 2023 2nd International Conference on Data Analytics, Computing and Artificial Intelligence (ICDACAI), Zakopane, Poland, 2023, pp. 502-508, doi: 10.1109/ICDACAI59742.2023.00101.
K. Rao, P. R. Gopal and K. Lata, "Computational Analysis of Machine Learning Algorithms to Predict Heart Disease," 2021 11th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Noida, India, 2021, pp. 960-964, doi: 10.1109/Confluence51648.2021.9377185.
Schober P, Vetter TR. Logistic Regression in Medical Research. Anesth Analg. 2021 Feb 1;132(2):365-366. doi: 10.1213/ANE.0000000000005247. PMID: 33449558; PMCID: PMC7785709.
A. Pandey and A. Jain, "Detection of Compromised Accounts using Machine Learning Based Boosting Algorithms- AdaBoost, XGBoost, and CatBoost," 2023 14th International Conference on Computing Communication and Networking Technologies (ICCCNT), Delhi, India, 2023, pp. 1-6, doi: 10.1109/ICCCNT56998.2023.10307557.
N. S. S. V. S. Rao and S. J. J. Thangaraj, "Flight Ticket Prediction using Random Forest Regressor Compared with Decision Tree Regressor," 2023 Eighth International Conference on Science Technology Engineering and Mathematics (ICONSTEM), Chennai, India, 2023, pp. 1-5, doi: 10.1109/ICONSTEM56934.2023.10142260.
N. Pachauri and C. W. Ahn, "Electrical Energy Prediction of Combined Cycle Power Plant Using Gradient Boosted Generalized Additive Model," in IEEE Access, vol. 10, pp. 24566-24577, 2022, doi: 10.1109/ACCESS.2022.3153720.
Khan, Z.A.; Ullah, A.; Ullah, W.; Rho, S.; Lee, M.; Baik, S.W. Electrical Energy Prediction in Residential Buildings for Short-Term Horizons Using Hybrid Deep Learning Strategy. Appl. Sci. 2020, 10, 8634. https://doi.org/10.3390/app10238634
Pierre AA, Akim SA, Semenyo AK, Babiga B. Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches. Energies. 2023; 16(12):4739. https://doi.org/10.3390/en16124739
Zhang, T., Liao, L., Lai, H., Liu, J., Zou, F., Cai, Q. (2019). Electrical Energy Prediction with Regression-Oriented Models. In: Krömer, P., Zhang, H., Liang, Y., Pan, JS. (eds) Proceedings of the Fifth Euro-China Conference on Intelligent Data Analysis and Applications. ECC 2018. Advances in Intelligent Systems and Computing, vol 891. Springer, Cham. https://doi.org/10.1007/978-3-030-03766-6_16
Publicado
2024-10-05
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