Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models

Ilustraciones

Autores:: Restrepo Gil, Adiel Ignacio

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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repository_id_str
dc.title.eng.fl_str_mv	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
dc.title.translated.eng.fl_str_mv	Pronóstico del consumo de electricidad horario para Antioquia-Colombia utilizando modelos de statistical-machine learning
title	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
spellingShingle	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 330 - Economía::333 - Economía de la tierra y de la energía Estadística - Procesamiento de datos Pronóstico del tiempo por estadística - Antioquia, Colombia Consumo de energía - Estadística - Antioquia, Colombia Rendimiento energético - Estadística - Antioquia, Colombia Forecast Electricity consumption Machine learning Pronóstico Consumo de electricidad
title_short	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
title_full	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
title_fullStr	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
title_full_unstemmed	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
title_sort	Hourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning models
dc.creator.fl_str_mv	Restrepo Gil, Adiel Ignacio
dc.contributor.advisor.none.fl_str_mv	Giraldo Gómez, Norman Diego
dc.contributor.author.none.fl_str_mv	Restrepo Gil, Adiel Ignacio
dc.subject.ddc.spa.fl_str_mv	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 330 - Economía::333 - Economía de la tierra y de la energía
topic	510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas 330 - Economía::333 - Economía de la tierra y de la energía Estadística - Procesamiento de datos Pronóstico del tiempo por estadística - Antioquia, Colombia Consumo de energía - Estadística - Antioquia, Colombia Rendimiento energético - Estadística - Antioquia, Colombia Forecast Electricity consumption Machine learning Pronóstico Consumo de electricidad
dc.subject.lemb.none.fl_str_mv	Estadística - Procesamiento de datos Pronóstico del tiempo por estadística - Antioquia, Colombia Consumo de energía - Estadística - Antioquia, Colombia Rendimiento energético - Estadística - Antioquia, Colombia
dc.subject.proposal.eng.fl_str_mv	Forecast Electricity consumption Machine learning
dc.subject.proposal.spa.fl_str_mv	Pronóstico Consumo de electricidad
description	Ilustraciones
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-07-09T12:17:40Z
dc.date.available.none.fl_str_mv	2024-07-09T12:17:40Z
dc.date.issued.none.fl_str_mv	2024
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
dc.type.driver.spa.fl_str_mv	info:eu-repo/semantics/masterThesis
dc.type.version.spa.fl_str_mv	info:eu-repo/semantics/acceptedVersion
dc.type.content.spa.fl_str_mv	Text
dc.type.redcol.spa.fl_str_mv	http://purl.org/redcol/resource_type/TM
status_str	acceptedVersion
dc.identifier.uri.none.fl_str_mv	https://repositorio.unal.edu.co/handle/unal/86415
dc.identifier.instname.spa.fl_str_mv	Universidad Nacional de Colombia
dc.identifier.reponame.spa.fl_str_mv	Repositorio Institucional Universidad Nacional de Colombia
dc.identifier.repourl.spa.fl_str_mv	https://repositorio.unal.edu.co/
url	https://repositorio.unal.edu.co/handle/unal/86415 https://repositorio.unal.edu.co/
identifier_str_mv	Universidad Nacional de Colombia Repositorio Institucional Universidad Nacional de Colombia
dc.language.iso.spa.fl_str_mv	eng
language	eng
dc.relation.indexed.spa.fl_str_mv	LaReferencia
dc.relation.references.spa.fl_str_mv	Arora, S. and Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for france. European Journal of Operational Research, 266(1):259–268. Bandara, K., Hyndman, R. J., and Bergmeir, C. (2021). Mstl: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns. Barrientos, A. F., Olaya, J., and Gonzalez, V. M. (2007). A spline model for electricity demand forescasting. Revista Colombiana de Estad ́ıstica, 30(2):187. Barrientos, J., Marquez Marulanda, L., and Villada Duque, F. (2023). Analyzing electricity demand in colombia: A functional time series approach. International Journal of Energy Economics and Policy, 13(1):75–84. Bartz, E., Bartz-Beielstein, T., Zaefferer, M., and Mersmann, O. (2023). Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide. Springer. Berbesi, L. and Pritchard, G. (2023). Modelling energy data in a generalized additive model: A case study of Colombia. Energies, 16(4). Bergmeir, C., Hyndman, R. J., and Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics Data Analysis, 120:70–83. Bontempi, G., Ben Taieb, S., and Le Borgne, Y.-A. (2013). Machine Learning Strategies for Time Series Forecasting, volume 138. Castaño, E. (2007). Reconstrucción de datos de series de tiempo: una aplicación a la demanda horaria de la electricidad. Revista Colombiana de Estadística, 30:247 – 263. Chapagain, K. and Kittipiyakul, S. (2018). Performance analysis of short-term electricity demand with atmospheric variables. Energies, 11. Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY, USA. Association for Computing Machinery. CNO (2021). Consejo nacional de operación: Acuerdo 1303 por el cual se actualizan los procedimientos para la gestión integral de la demanda. Retrieved Dec 10, 2023 from https://bit.ly/3KSCHoI. CPC (2020). Informe Nacional de Competitividad 2020-2021. Bogotá: Consejo Privado de Competitividad. Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy. Journal of Business Economic Statistics, 13(3):253–63. Diebold, F. X. and Rudebusch, G. D. (1999). Business cycles: Durations, dynamics, and forecasting. (6636). Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., and Torres, J. F. (2018). Stacking ensemble learning for short-term electricity consumption forecasting. Energies, 11(4). DNP (2017). Energy Demand Situation in Colombia. Enersinc. EIA (2023). Energy Information Administration: Electricity explained. Retrieved Dec 1, 2023 from www.eia.gov/energyexplained/electricity. Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2):251–276. Garcia, J., Rey Londoño, F., Arango Restrepo, L. J., and Bohorquez Correa, S. (2023). Sectoral analysis of electricity consumption during the covid-19 pandemic: Evidence for unregulated and regulated markets in colombia. Energy, 268:126614. Gómez, D. (2001). Análisis espectral: consideraciones teóricas y aplicabilidad. Economía y Sociedad, 6. Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. Hamilton, J. D. (1994). Time series analysis. 10. Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2):281–291. Hastie, T. and Tibshirani, R. (2017). Generalized Additive Models. Taylor Francis Group., pages 249–307. Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics. Springer, 2nd ed. 2009. corr. 3rd printing 5th printing. edition. Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman Hall/CRC Monographs on Statistics Applied Probability. Chapman and Hall/CRC. Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9:1735–80. James, G., Tibshirani, R., Witten, D., and Hastie, T. (2021). An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer, 2 edition. Jiang, P., Li, R., Lu, H., and Zhang, X. (2020). Modeling of electricity demand forecast for power system. Neural Computing and Applications, 32:1–19. Jimenez Mares, J., Pertuz, A., Quintero M., C., and Montana, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17:93–101. Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59(6):1551–1580. Khalil, M., McGough, S. A., Pourmirza, Z., Pazhoohesh, M., and Walker, S. (2022). Machine learning, deep learning and statistical analysis for forecasting building energy consumption — a systematic review. Engineering Applications of Artificial Intelligence, 115:105287. Khan, A. M. and Osi ́nska, M. (2023). Comparing forecasting accuracy of selected grey and time series models based on energy consumption in brazil and india. Expert Systems with Applications, 212:118840. Kim, Y.-J. and Gu, C. (2004). Smoothing spline gaussian regression: More scalable computation via efficient approximation. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 66(2):337–356. Kumar, S., Viral, R., Deep, V., Sharma, P., Kumar, M., Mahmud, M., and Stephan, T. (2021). Forecasting major impacts of covid-19 pandemic on country-driven sectors: challenges, lessons, and future roadmap. Personal and Ubiquitous Computing. La Tona, G., Luna, M., and Di Piazza, M. (2023). Day-ahead forecasting of residential electric power consumption for energy management using long short-term memory encoder–decoder model. Mathematics and Computers in Simulation. Li, R., Chen, X., Balezentis, T., Streimikiene, D., and Niu, Z. (2021). Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Comput. Appl., 33(1):301–320. Maltais, L.-G. and Gosselin, L. (2022). Forecasting of short-term lighting and plug load electricity consumption in single residential units: Development and assessment of data-driven models for different horizons. Applied Energy, 307:118229. Medina, S., Moreno, J., and Gallego, J. P. (2012). Pronóstico de la demanda de energía eléctrica horaria en Colombia mediante redes neuronales artificiales. Revista Facultad de Ingeniería Universidad de Antioquia, (59):98–107. Meng, Z., Sun, H., and Wang, X. (2022). Forecasting energy consumption based on svr and markov model: A case study of china. Frontiers in Environmental Science, page 363. Murat, N. (2022). Outlier detection in statistical modeling via multivariate adaptive regression splines. Communications in Statistics - Simulation and Computation, 0(0):1–12. Murillo, J., Trejos, A., and Carvajal, P. (2003). Estudio del pronostico de la demanda de energía eléctrica, utilizando modelos de series de tiempo. Scientia Et Technica, 3(23). Peters, J., Janzing, D., and Schlkopf, B. (2017). Elements of Causal Inference: Foundations and Learning Algorithms. The MIT Press. Ribeiro, A., Carmo, P., Silva, I., Sadok, D., Lynn, T., and Endo, P. (2020). Short-term firm-level energy-consumption forecasting for energy-intensive manufacturing: A comparison of machine learning and deep learning models. Algorithms, 13:274. Rosero, J., Gonzalez, F. A., Zambrano, A., and Toledo-Cortesa, S. (2023). Short-term forecasting of power demand: A case study with data from colombia. SSNR. Sadeghian Broujeny, R., Ben Ayed, S., and Matalah, M. (2023). Energy consumption forecasting in a university office by artificial intelligence techniques: An analysis of the exogenous data effect on the modeling. Energies, 16(10). Safer, A. M. (2004). Multivariate adaptive regression splines and insider trading data for stock prediction. Journal of Interdisciplinary Mathematics, 7(1):79–93. Santa María, M., Von Der Fehr, N.-H., Millán, J., Benavides, J., and Gracia, O. (2009). El mercado de la energía eléctrica en Colombia: características, evolución e impacto sobre otros sectores. Bogotá: Fedesarrollo, 304 p. Saranj, A. and Zolfaghari, M. (2022). The electricity consumption forecast: Adopting a hybrid approach by deep learning and arimax-garch models. Energy Reports, 8:7657–7679. Sarmiento, H. O. and Villa, W. M. (2008). Inteligencia artificial en pronóstico de demanda de energía eléctrica: una aplicación en optimización de recursos energéticos. Revista Colombiana de Tecnologías de Avanzada. Son, J., Cha, J., Kim, H., and Wi, Y.-M. (2022). Day-ahead short-term load forecasting for holidays based on modification of similar days’ load profiles. IEEE Access, 10:17864– 17880. Sujan, R. A., Akashdeep, S., Harshvardhan, R., and Sowmya, K. S. (2022). Stacking deep learning and machine learning models for short-term energy consumption forecasting. Advanced Engineering Informatics, 52:101542. UPME (2021). Proyección de Demanda de Energía Eléctrica, Gas Natural y Combustibles Líquidos 2022-2036. UPME (2023). Proyecciión de la Demanda de Energía Eléctrica y Potencia Máxima 2023- 2037. Ushiku, Y. (2020). Long Short-Term Memory, pages 1–7. Springer International Publishing, Cham. Valencia, A., Moreno, C. A., and Lozano, C. A. (2007). Modelo de promedios móviles para el pronóstico horario de potencia y energía eléctrica. El Hombre y la Máquina Wasserman, L. (2010). All of Statistics : a concise course in statistical inference. Springer, New York. Wen, L., Zhou, K., and Yang, S. (2020). Load demand forecasting of residential buildings using a deep learning model. Electric Power Systems Research, 179:106073. Wood, S. N. (2017). Generalized Additive Models: An Introduction with R, Second Edition. XM (2023). Mercados. Retrieved Dec 10, 2023 from www.xm.com.co/consumo/mercados. Young-Min, W., Sung-Kwan, J., and Kyung-Bin, S. (2012). Holiday load forecasting using fuzzy polynomial regression with weather feature selection and adjustment. IEEE Transactions on Power Systems, 27(2):596–603. Zhou, C., Fang, Z., Xu, X., Zhang, X., Ding, Y., Jiang, X., and ji, Y. (2020). Using long short-term memory networks to predict energy consumption of air-conditioning systems. Sustainable Cities and Society, 55:102000. Zhou, W., Tao, H., Ding, S., and Li, Y. (2023). Electricity consumption and production forecasting considering seasonal patterns: An investigation based on a novel seasonal discrete grey model. Journal of the Operational Research Society, 74(5):1346–1361. Zhu, G., Peng, S., Lao, Y., Su, Q., and Sun, Q. (2021a). Short-term electricity consumption forecasting based on the emd-fbprophet-lstm method. Mathematical Problems in Engineering, 2021:1–9. Zhu, K., Geng, J., and Wang, K. (2021b). A hybrid prediction model based on pattern sequence-based matching method and extreme gradient boosting for holiday load forecasting. Electric Power Systems Research, 190:106841. Zolfaghari, M. and Sahabi, B. (2019). A hybrid approach to model and forecast the electricity consumption by neurowavelet and arimax-garch models. Energy Efficiency, 12.
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dc.rights.license.spa.fl_str_mv	Atribución-NoComercial-CompartirIgual 4.0 Internacional
dc.rights.uri.spa.fl_str_mv	http://creativecommons.org/licenses/by-nc-sa/4.0/
dc.rights.accessrights.spa.fl_str_mv	info:eu-repo/semantics/openAccess
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dc.format.extent.spa.fl_str_mv	50 páginas
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dc.coverage.region.none.fl_str_mv	Antioquia, Colombia
dc.publisher.spa.fl_str_mv	Universidad Nacional de Colombia
dc.publisher.program.spa.fl_str_mv	Medellín - Ciencias - Maestría en Ciencias - Estadística
dc.publisher.faculty.spa.fl_str_mv	Facultad de Ciencias
dc.publisher.place.spa.fl_str_mv	Medellín, Colombia
dc.publisher.branch.spa.fl_str_mv	Universidad Nacional de Colombia - Sede Medellín
institution	Universidad Nacional de Colombia
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spelling	Atribución-NoComercial-CompartirIgual 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Giraldo Gómez, Norman Diego1fe8759b9fc1218bec22a4b4a06a0ff7Restrepo Gil, Adiel Ignacioe8454d9b4a230537d39bbc255783e5b42024-07-09T12:17:40Z2024-07-09T12:17:40Z2024https://repositorio.unal.edu.co/handle/unal/86415Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/IlustracionesEnergy sector plays a fundamental role in encouraging a country's economic growth and social progress due to its functionality as an input for productive processes and as a public service asset that provides greater welfare to the population. Electricity consumption forecasting is a valuable instrument for policy-makers to guide pricing, taxation and investment decisions, as well as energy and operational security planning, helping to ensure a continuous supply of electricity and reducing cost overruns associated with the provision of energy distribution services. The aim of this research is to forecast hourly electricity consumption in Antioquia-Colombia using Statiscal-Machine Learning models with exogenous variables such as day-type and maximum temperature. The results show that LSTM Neural Network can be an efficient model for the operational deployment of electricity distribution since its average electricity supply error for an operational week is estimated to be around 493 MWh, while XM Market Operator's benchmark model obtained an error of 3420 MWh during the evaluated week. (Tomado de la fuente)El sector energético desempeña un papel fundamental en el fomento del crecimiento económico y el progreso social de un país debido a su funcionalidad como insumo de los procesos productivos y como activo de servicio público que proporciona mayor bienestar a la población. La previsión del consumo de energía eléctrica es un valioso instrumento para que los hacedores de política orienten las decisiones de tarifas, impuestos e inversión, así como la planificación de la seguridad energética y operativa, contribuyendo a garantizar un suministro continuo de electricidad y reduciendo los sobrecostos asociados a la prestación de los servicios de distribución de energía. El objetivo de esta investigación es pronosticar el consumo de electricidad horario en Antioquia-Colombia utilizando modelos de Statistical-Machine Learning con variables exógenas como el tipo de día y la temperatura máxima. Los resultados muestran que la Red Neuronal LSTM puede ser un modelo eficiente para el despliegue operativo de la distribución eléctrica debido a que su error promedio de suministro de electricidad para una semana operativa se estima en alrededor de 493 MWh, mientras que el modelo de referencia del Operador de Mercado XM obtuvo un error de 3420 MWh durante la semana evaluada.MaestríaMagíster en Ciencias - EstadísticaEstadística.Sede Medellín50 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas330 - Economía::333 - Economía de la tierra y de la energíaEstadística - Procesamiento de datosPronóstico del tiempo por estadística - Antioquia, ColombiaConsumo de energía - Estadística - Antioquia, ColombiaRendimiento energético - Estadística - Antioquia, ColombiaForecastElectricity consumptionMachine learningPronósticoConsumo de electricidadHourly electricity consumption forecasting for Antioquia-Colombia using statistical-machine learning modelsPronóstico del consumo de electricidad horario para Antioquia-Colombia utilizando modelos de statistical-machine learningTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMAntioquia, ColombiaLaReferenciaArora, S. and Taylor, J. W. (2018). Rule-based autoregressive moving average models for forecasting load on special days: A case study for france. European Journal of Operational Research, 266(1):259–268.Bandara, K., Hyndman, R. J., and Bergmeir, C. (2021). Mstl: A seasonal-trend decomposition algorithm for time series with multiple seasonal patterns.Barrientos, A. F., Olaya, J., and Gonzalez, V. M. (2007). A spline model for electricity demand forescasting. Revista Colombiana de Estad ́ıstica, 30(2):187.Barrientos, J., Marquez Marulanda, L., and Villada Duque, F. (2023). Analyzing electricity demand in colombia: A functional time series approach. International Journal of Energy Economics and Policy, 13(1):75–84.Bartz, E., Bartz-Beielstein, T., Zaefferer, M., and Mersmann, O. (2023). Hyperparameter Tuning for Machine and Deep Learning with R - A Practical Guide. Springer.Berbesi, L. and Pritchard, G. (2023). Modelling energy data in a generalized additive model: A case study of Colombia. Energies, 16(4).Bergmeir, C., Hyndman, R. J., and Koo, B. (2018). A note on the validity of cross-validation for evaluating autoregressive time series prediction. Computational Statistics Data Analysis, 120:70–83.Bontempi, G., Ben Taieb, S., and Le Borgne, Y.-A. (2013). Machine Learning Strategies for Time Series Forecasting, volume 138.Castaño, E. (2007). Reconstrucción de datos de series de tiempo: una aplicación a la demanda horaria de la electricidad. Revista Colombiana de Estadística, 30:247 – 263.Chapagain, K. and Kittipiyakul, S. (2018). Performance analysis of short-term electricity demand with atmospheric variables. Energies, 11.Chen, T. and Guestrin, C. (2016). Xgboost: A scalable tree boosting system. In Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’16, page 785–794, New York, NY, USA. Association for Computing Machinery.CNO (2021). Consejo nacional de operación: Acuerdo 1303 por el cual se actualizan los procedimientos para la gestión integral de la demanda. Retrieved Dec 10, 2023 from https://bit.ly/3KSCHoI.CPC (2020). Informe Nacional de Competitividad 2020-2021. Bogotá: Consejo Privado de Competitividad.Diebold, F. and Mariano, R. (1995). Comparing predictive accuracy. Journal of Business Economic Statistics, 13(3):253–63.Diebold, F. X. and Rudebusch, G. D. (1999). Business cycles: Durations, dynamics, and forecasting. (6636).Divina, F., Gilson, A., Goméz-Vela, F., García Torres, M., and Torres, J. F. (2018). Stacking ensemble learning for short-term electricity consumption forecasting. Energies, 11(4).DNP (2017). Energy Demand Situation in Colombia. Enersinc.EIA (2023). Energy Information Administration: Electricity explained. Retrieved Dec 1, 2023 from www.eia.gov/energyexplained/electricity.Engle, R. F. and Granger, C. W. J. (1987). Co-integration and error correction: Representation, estimation, and testing. Econometrica, 55(2):251–276.Garcia, J., Rey Londoño, F., Arango Restrepo, L. J., and Bohorquez Correa, S. (2023). Sectoral analysis of electricity consumption during the covid-19 pandemic: Evidence for unregulated and regulated markets in colombia. Energy, 268:126614.Gómez, D. (2001). Análisis espectral: consideraciones teóricas y aplicabilidad. Economía y Sociedad, 6.Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press.Hamilton, J. D. (1994). Time series analysis. 10.Harvey, D., Leybourne, S., and Newbold, P. (1997). Testing the equality of prediction mean squared errors. International Journal of Forecasting, 13(2):281–291.Hastie, T. and Tibshirani, R. (2017). Generalized Additive Models. Taylor Francis Group., pages 249–307.Hastie, T., Tibshirani, R., and Friedman, J. (2009). The elements of statistical learning: Data mining, inference, and prediction. Springer Series in Statistics. Springer, 2nd ed. 2009. corr. 3rd printing 5th printing. edition.Hastie, T., Tibshirani, R., and Wainwright, M. (2015). Statistical Learning with Sparsity: The Lasso and Generalizations. Chapman Hall/CRC Monographs on Statistics Applied Probability. Chapman and Hall/CRC.Hochreiter, S. and Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9:1735–80.James, G., Tibshirani, R., Witten, D., and Hastie, T. (2021). An Introduction to Statistical Learning: With Applications in R. Springer Texts in Statistics. Springer, 2 edition.Jiang, P., Li, R., Lu, H., and Zhang, X. (2020). Modeling of electricity demand forecast for power system. Neural Computing and Applications, 32:1–19.Jimenez Mares, J., Pertuz, A., Quintero M., C., and Montana, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17:93–101.Johansen, S. (1991). Estimation and hypothesis testing of cointegration vectors in gaussian vector autoregressive models. Econometrica, 59(6):1551–1580.Khalil, M., McGough, S. A., Pourmirza, Z., Pazhoohesh, M., and Walker, S. (2022). Machine learning, deep learning and statistical analysis for forecasting building energy consumption — a systematic review. Engineering Applications of Artificial Intelligence, 115:105287.Khan, A. M. and Osi ́nska, M. (2023). Comparing forecasting accuracy of selected grey and time series models based on energy consumption in brazil and india. Expert Systems with Applications, 212:118840.Kim, Y.-J. and Gu, C. (2004). Smoothing spline gaussian regression: More scalable computation via efficient approximation. Journal of the Royal Statistical Society. Series B (Statistical Methodology), 66(2):337–356.Kumar, S., Viral, R., Deep, V., Sharma, P., Kumar, M., Mahmud, M., and Stephan, T. (2021). 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Energy Efficiency, 12.AdministradoresEstudiantesInvestigadoresPúblico generalResponsables políticosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/86415/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1037664686.2024.pdf1037664686.2024.pdfTésis de Maestría en Ciencias - Estadísticaapplication/pdf2493851https://repositorio.unal.edu.co/bitstream/unal/86415/2/1037664686.2024.pdfcd39d2c1506969818c0eadfa328a0663MD52THUMBNAIL1037664686.2024.pdf.jpg1037664686.2024.pdf.jpgGenerated Thumbnailimage/jpeg4683https://repositorio.unal.edu.co/bitstream/unal/86415/3/1037664686.2024.pdf.jpg414bdbeb6bf84fd10fd446bd2330b7b6MD53unal/86415oai:repositorio.unal.edu.co:unal/864152024-07-09 23:12:15.252Repositorio Institucional Universidad Nacional de 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