Probabilistic forecasting of electricity demand in Colombia

ilustraciones, gráficos

Autores:: Mosquera Cabra, Jennifer

Tipo de recurso:

Fecha de publicación:: 2024

Institución:: Universidad Nacional de Colombia

Repositorio:: Universidad Nacional de Colombia

Idioma:: eng

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dc.title.eng.fl_str_mv	Probabilistic forecasting of electricity demand in Colombia
dc.title.translated.spa.fl_str_mv	Pronóstico probabilístico de la demanda de electricidad en Colombia
title	Probabilistic forecasting of electricity demand in Colombia
spellingShingle	Probabilistic forecasting of electricity demand in Colombia 000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::515 - Análisis 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Demanda de energía eléctrica - Colombia Distribución de energía eléctrica - Colombia Abastecimiento de energía - Colombia Análisis de series de tiempo Probabilidades - Procesamiento de datos electricity demand prediction intervals uncertainty quantification Bootstrapping conformal prediction time series modeling demanda de electricidad intervalos de predicción cuantificación de incertidumbre predicción conforme modelización de series temporales
title_short	Probabilistic forecasting of electricity demand in Colombia
title_full	Probabilistic forecasting of electricity demand in Colombia
title_fullStr	Probabilistic forecasting of electricity demand in Colombia
title_full_unstemmed	Probabilistic forecasting of electricity demand in Colombia
title_sort	Probabilistic forecasting of electricity demand in Colombia
dc.creator.fl_str_mv	Mosquera Cabra, Jennifer
dc.contributor.advisor.none.fl_str_mv	López Ríos, Víctor Ignacio Gallón Gómez, Santiago
dc.contributor.author.none.fl_str_mv	Mosquera Cabra, Jennifer
dc.subject.ddc.spa.fl_str_mv	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::515 - Análisis 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas
topic	000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores 510 - Matemáticas::515 - Análisis 510 - Matemáticas::519 - Probabilidades y matemáticas aplicadas Demanda de energía eléctrica - Colombia Distribución de energía eléctrica - Colombia Abastecimiento de energía - Colombia Análisis de series de tiempo Probabilidades - Procesamiento de datos electricity demand prediction intervals uncertainty quantification Bootstrapping conformal prediction time series modeling demanda de electricidad intervalos de predicción cuantificación de incertidumbre predicción conforme modelización de series temporales
dc.subject.lemb.none.fl_str_mv	Demanda de energía eléctrica - Colombia Distribución de energía eléctrica - Colombia Abastecimiento de energía - Colombia Análisis de series de tiempo Probabilidades - Procesamiento de datos
dc.subject.proposal.eng.fl_str_mv	electricity demand prediction intervals uncertainty quantification Bootstrapping conformal prediction time series modeling
dc.subject.proposal.spa.fl_str_mv	demanda de electricidad intervalos de predicción cuantificación de incertidumbre predicción conforme modelización de series temporales
description	ilustraciones, gráficos
publishDate	2024
dc.date.accessioned.none.fl_str_mv	2024-04-18T15:43:41Z
dc.date.available.none.fl_str_mv	2024-04-18T15:43:41Z
dc.date.issued.none.fl_str_mv	2024-04-09
dc.type.spa.fl_str_mv	Trabajo de grado - Maestría
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status_str	acceptedVersion
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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/85944 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	Al-Musaylh, M. S., Deo, R. C., Adamowski, J. F., and Li, Y. (2018). Short-term electricity demand forecasting with mars, svr and arima models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35(16):1–16. Amat Rodrigo, J. and Escobar Ortiz, J. (2023). skforecast (version 0.11.0) [computer software]. https://doi.org/10.5281/zenodo.8382788. Bedi, J. and Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238(C):1312–1326. Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32. Brownlee, J. (2017). Introduction to Time Series Forecasting With Python: How to Prepare Data and Develop Models to Predict the Future. Machine Learning Mastery (Publisher). 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. ACM. Clements, M. P. and Kim, J. H. (2007). Bootstrap prediction intervals for autoregressive time series. Computational Statistics Data Analysis, 51(7):3580–3594. Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Number 57 in Monographs on Statistics and Applied Probability. Chapman & Hall/CRC, Boca Raton, Florida, USA. Fabbiani, E., Marziali, A., and Nicolao, G. D. (2021). Ensembling methods for countrywide short-term forecasting of gas demand. International Journal of Oil, Gas and Coal Technology, 26(2):184. Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2):337–407. Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 29(5):1189–1232. Friedman, J. H., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1–22. Gao, T., Niu, D., Ji, Z., and Sun, L. (2022). Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm. Energy, Elsevier, vol. 261(PB). Gneiting, T. and Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1):125–151. Guo, Z., Zhou, K., Zhang, X., and Yang, S. (2018). A deep learning model for short-term power load and probability density forecasting. Energy, 160(C):1186–1200. Huurman, C., Ravazzolo, F., and Zhou, C. (2012). The power of weather. Computational Statistics Data Analysis, 56(11):3793–3807. 1st issue of the Annals of Computational and Financial Econometrics Sixth Special Issue on Computational Econometrics. Jensen, V., Bianchi, F. M., and Anfinsen, S. N. (2024). Ensemble conformalized quantile regression for probabilistic time series forecasting. 1-12. Jim´enez, J., Pertuz, A., Quintero, C., and Monta˜na, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17(01):93–101. Kath, C. and Ziel, F. (2021). Conformal prediction interval estimation and applications to day-ahead and intraday power markets. International Journal of Forecasting, 37(2):: 777–799. Khosravi, A., Nahavandi, S., and Creighton, D. (2013). Quantifying uncertainties of neural network-based electricity price forecasts. Applied Energy, 112:120–129. Kilian, L. (1998). Small-sample confidence intervals for impulse response functions. The Review of Economics and Statistics, 80(2):218–230. Li, R., Chen, X., Baleentis, T., Treimikien˙e, D., and Niu, Z. (2020). Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Computing and Applications, 33:301–320. Maciejowska, K., Nowotarski, J., and Weron, R. (2016). Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. International Journal of Forecasting, 32(3):957–965. Marino, A., Arango., A., Lotero, L., and Jimenez, M. (2021). Modelos de series temporales para pron´ostoco de la demanda el´ectrica del sector de explotaci´on de minas y canteras en Colombia. Revista EIA, 18:77 – 99. Masarotto, G. (1990). Bootstrap prediction intervals for autoregressions. International Journal of Forecasting, 6(2):229–239. Misiorek, A., Trueck, S., and Weron, R. (2006). Point and interval forecasting of spot electricity prices: Linear vs. non-linear time series models. Studies in Nonlinear Dynamics Econometrics, 10(3):1–34. Mohammed, A. and Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2):757–774. Nielsen, D. (2016). Tree boosting with xgboost-why does xgboost win every machine learning competition. Master’s thesis, Norwegian University of Science and Technology. Ning, Y., Zhao, R., Wang, S., Yuan, B., Wang, Y., and Zheng, D. (2022). Probabilistic short-term power load forecasting based on b-scn. Energy Reports, 8:646–655. The 2022 International Conference on Energy Storage Technology and Power Systems. Nowotarski, J. and Weron, R. (2013). Computing electricity spot price prediction intervals using quantile regression and forecast averaging. HSC Research Reports HSC/13/12, Hugo Steinhaus Center, Wroclaw University of Technology. Nowotarski, J. and Weron, R. (2018). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, 81:1548– 1568. Panagiotelis, A. and Smith, M. (2008). Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions. International Journal of Forecasting, 24(4):710–727. Pascual, L., Romo, J., and Ruiz, E. (2005). Bootstrap prediction intervals for powertransformed time series. International Journal of Forecasting, 21:219–235. Ramachandran, P., Zoph, B., and Le, Q. V. (2017). Searching for activation functions. retrieved from https://arxiv.org/abs/1710.05941. Son, N., Yang, S., and Na, J. (2020). Deep neural network and long short-term memory for electric power load forecasting. Applied Sciences, 10(18):6489. Stankeviciute, K., M. Alaa, A., and van der Schaar, M. (2021). Conformal time-series forecasting. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages : 6216–6228. Curran Associates, Inc. Ullah, F. U. M., Ullah, A., Khan, N., Lee, M. Y., Rho, S., Baik, S. W., and Bai, X. (2022). Deep Learning-Assisted Short-Term Power Load Forecasting Using Deep Convolutional LSTM and Stacked GRU. Complexity, 2022:1–15. Weron, R. and Misiorek, A. (2008). Forecasting spot electricity prices: A comparison of parametric and semiparametric time series models. International Journal of Forecasting, 24(4):744–763. Xu, C. and Xie, Y. (2023). Conformal prediction for time series. Preprint. https://arxiv.org/pdf/2010.09107.pdf. Zhang, A., Lipton, Z. C., Li, M., and Smola, A. J. (2023). Dive into Deep Learning. Cambridge University Press. https://D2L.ai. Zhang, Y., Liu, K., Liang, Q., and An, X. (2016). Deterministic and probabilistic interval prediction for short-term wind power generation based on variational mode decomposition and machine learning methods. Energy Conversion and Management, 112:208–219. Zhao, J. H., Dong, Z. Y., Xu, Z., and Wong, K. P. (2008). A statistical approach for interval forecasting of the electricity price. IEEE Transactions on Power Systems, 23(2):267 – 276. Zou, H. and Hastie, T. (2005). Regularization and variable selection via the elastic net. Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301– 320.
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dc.format.extent.spa.fl_str_mv	79 páginas
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dc.coverage.country.none.fl_str_mv	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	Reconocimiento 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2López Ríos, Víctor Ignacioe016d272b8b57062b987c9e24058bdfcGallón Gómez, Santiagob30fc96328aa98681377ec6f5f4e0257Mosquera Cabra, Jennifera92cda3ee41f0e72d957f46f173c10482024-04-18T15:43:41Z2024-04-18T15:43:41Z2024-04-09https://repositorio.unal.edu.co/handle/unal/85944Universidad Nacional de ColombiaRepositorio Institucional Universidad Nacional de Colombiahttps://repositorio.unal.edu.co/ilustraciones, gráficosNew approaches have emerged in the field of uncertainty measurement, offering ways to estimate models and their corresponding confidence levels for point predictions. Our first purpose is to compare the predictive capabilities of some models built for forecasting daily electricity demand in Colombia. Initially, we employ generalized linear models, followed by Machine Learning models such as ensemble learning models, support vector machines (SVM), and finally deep learning models. The goal is to determine which model demonstrates superior predictive accuracy in forecasting daily electricity demand in Colombia. In order to evaluate their performance, we mainly use Mean Absolute Percentage Error (MAPE) as a comprehensive measure, which allows us to evaluate their effectiveness in capturing the actual demand values. And also take into account the mean absolute error (MAE) and the root mean squared error (RMSE). Next, we turn our attention on the creation of prediction intervals to handle the uncertainty in our forecasts. We use techniques like Bootstrapping to figure out these intervals. We also incorporate conformal prediction to improve the reliability of our intervals. Our prediction intervals are evaluated primarily based on their coverage percentage. This will allow us to see how frequently our prediction intervals correspond to the actual demand from this data. Through this combination of methods, our goal is to establish a robust and user-friendly framework for forecasting daily electricity demand in Colombia. The results of this development suggest that (1) for the daily energy demand of Colombia, with the variables obtained at a daily frequency, a simple model such as a regularized model works better than an advanced and much more complex model such as a deep learning model. (2) Regarding feature selection concerns, the most important variables are the energy demand lags and demand structure variables for the Lasso model, which works as a feature selection method, due to its regularization nature. This confirms that the inclusion of lags or having an autocorrelated structure is important in this type of problem. Finally, for the forecast intervals, in which we used two methods, the first and most common was the bootstrap method and the second, whose development is more recent, is the conformal Prediction. The construction of our prediction intervals allowed us to give a 99 % confidence level to the point prediction and not just rely on the comparison between the actual and predicted values. (Tomado de la fuente)Han surgido nuevos enfoques en el campo de la medición de la incertidumbre, que ofrecen formas de estimar modelos y sus correspondientes niveles de confianza para predicciones puntuales. Nuestro primer propósito es comparar las capacidades predictivas de algunos modelos construidos para pronosticar la demanda diaria de electricidad en Colombia. Inicialmente, empleamos modelos lineales generalizados, seguidos de modelos de Machine Learning tales como modelos de aprendizaje ensemble, máquinas de vectores soporte (SVM), y finalmente modelos de aprendizaje profundo. El objetivo es determinar qué modelo demuestra una precisión predictiva superior en el pronóstico de la demanda diaria de electricidad en Colombia. Para evaluar su desempeño se utiliza principalmente el Error Porcentual Absoluto Medio (MAPE) como medida integral, que permite evaluar su efectividad para capturar los valores reales de demanda. También tenemos en cuenta el error medio absoluto (MAE) y el error cuadrático medio (RMSE). A continuación, centramos nuestra atención en la creación de intervalos de predicción para manejar la incertidumbre de nuestras previsiones. Para calcular estos intervalos utilizamos técnicas como el Bootstrapping. También incorporamos la predicción conforme para mejorar la fiabilidad de nuestros intervalos. Nuestros intervalos de predicción se evalúan principalmente en función de su porcentaje de cobertura. Esto nos permitirá ver con qué frecuencia nuestros intervalos de predicción se corresponden con la demanda real a partir de estos datos. Mediante esta combinación de métodos, nuestro objetivo es establecer un marco robusto y fácil de usar para la predicción de la demanda diaria de electricidad en Colombia. Los resultados de este desarrollo sugieren que (1) para la demanda diaria de energía de Colombia, con las variables obtenidas a una frecuencia diaria, un modelo simple como un modelo regularizado funciona mejor que un modelo avanzado y mucho más complejo como un modelo de aprendizaje profundo. (2) En cuanto a las preocupaciones de selección de características, las variables más importantes son los rezagos de demanda de energía y las variables de estructura de demanda para el modelo Lasso, que funciona como método de selección de características, debido a su naturaleza de regularización. Esto confirma que la inclusión de retardos o tener una estructura autocorrelacionada es importante en este tipo de problemas. Por último, para los intervalos de predicción, en los que utilizamos dos métodos, el primero y más común fue el método bootstrap y el segundo, cuyo desarrollo es más reciente, es la Predicción conforme. La construcción de nuestros intervalos de predicción nos permitió dar un nivel de confianza del 99% a la predicción puntual y no basarnos únicamente en la comparación entre los valores reales y los predichos.MaestríaMagíster en Ciencias - EstadísticaEstadística.Sede Medellín79 páginasapplication/pdfengUniversidad Nacional de ColombiaMedellín - Ciencias - Maestría en Ciencias - EstadísticaFacultad de CienciasMedellín, ColombiaUniversidad Nacional de Colombia - Sede Medellín000 - Ciencias de la computación, información y obras generales::004 - Procesamiento de datos Ciencia de los computadores510 - Matemáticas::515 - Análisis510 - Matemáticas::519 - Probabilidades y matemáticas aplicadasDemanda de energía eléctrica - ColombiaDistribución de energía eléctrica - ColombiaAbastecimiento de energía - ColombiaAnálisis de series de tiempoProbabilidades - Procesamiento de datoselectricity demandprediction intervalsuncertainty quantificationBootstrappingconformal predictiontime series modelingdemanda de electricidadintervalos de prediccióncuantificación de incertidumbrepredicción conformemodelización de series temporalesProbabilistic forecasting of electricity demand in ColombiaPronóstico probabilístico de la demanda de electricidad en ColombiaTrabajo de grado - Maestríainfo:eu-repo/semantics/masterThesisinfo:eu-repo/semantics/acceptedVersionTexthttp://purl.org/redcol/resource_type/TMColombiaLaReferenciaAl-Musaylh, M. S., Deo, R. C., Adamowski, J. F., and Li, Y. (2018). Short-term electricity demand forecasting with mars, svr and arima models using aggregated demand data in Queensland, Australia. Advanced Engineering Informatics, 35(16):1–16.Amat Rodrigo, J. and Escobar Ortiz, J. (2023). skforecast (version 0.11.0) [computer software]. https://doi.org/10.5281/zenodo.8382788.Bedi, J. and Toshniwal, D. (2019). Deep learning framework to forecast electricity demand. Applied Energy, 238(C):1312–1326.Breiman, L. (2001). Random forests. Machine learning, 45(1):5–32.Brownlee, J. (2017). Introduction to Time Series Forecasting With Python: How to Prepare Data and Develop Models to Predict the Future. Machine Learning Mastery (Publisher).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. ACM.Clements, M. P. and Kim, J. H. (2007). Bootstrap prediction intervals for autoregressive time series. Computational Statistics Data Analysis, 51(7):3580–3594.Efron, B. and Tibshirani, R. J. (1993). An Introduction to the Bootstrap. Number 57 in Monographs on Statistics and Applied Probability. Chapman & Hall/CRC, Boca Raton, Florida, USA.Fabbiani, E., Marziali, A., and Nicolao, G. D. (2021). Ensembling methods for countrywide short-term forecasting of gas demand. International Journal of Oil, Gas and Coal Technology, 26(2):184.Friedman, J., Hastie, T., and Tibshirani, R. (2000). Additive logistic regression: a statistical view of boosting (with discussion and a rejoinder by the authors). The annals of statistics, 28(2):337–407.Friedman, J. H. (2001). Greedy function approximation: a gradient boosting machine. Annals of statistics, 29(5):1189–1232.Friedman, J. H., Hastie, T., and Tibshirani, R. (2010). Regularization paths for generalized linear models via coordinate descent. Journal of Statistical Software, 33(1):1–22.Gao, T., Niu, D., Ji, Z., and Sun, L. (2022). Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm. Energy, Elsevier, vol. 261(PB).Gneiting, T. and Katzfuss, M. (2014). Probabilistic forecasting. Annual Review of Statistics and Its Application, 1(1):125–151.Guo, Z., Zhou, K., Zhang, X., and Yang, S. (2018). A deep learning model for short-term power load and probability density forecasting. Energy, 160(C):1186–1200.Huurman, C., Ravazzolo, F., and Zhou, C. (2012). The power of weather. Computational Statistics Data Analysis, 56(11):3793–3807. 1st issue of the Annals of Computational and Financial Econometrics Sixth Special Issue on Computational Econometrics.Jensen, V., Bianchi, F. M., and Anfinsen, S. N. (2024). Ensemble conformalized quantile regression for probabilistic time series forecasting. 1-12.Jim´enez, J., Pertuz, A., Quintero, C., and Monta˜na, J. (2019). Multivariate statistical analysis based methodology for long-term demand forecasting. IEEE Latin America Transactions, 17(01):93–101.Kath, C. and Ziel, F. (2021). Conformal prediction interval estimation and applications to day-ahead and intraday power markets. International Journal of Forecasting, 37(2):: 777–799.Khosravi, A., Nahavandi, S., and Creighton, D. (2013). Quantifying uncertainties of neural network-based electricity price forecasts. Applied Energy, 112:120–129.Kilian, L. (1998). Small-sample confidence intervals for impulse response functions. The Review of Economics and Statistics, 80(2):218–230.Li, R., Chen, X., Baleentis, T., Treimikien˙e, D., and Niu, Z. (2020). Multi-step least squares support vector machine modeling approach for forecasting short-term electricity demand with application. Neural Computing and Applications, 33:301–320.Maciejowska, K., Nowotarski, J., and Weron, R. (2016). Probabilistic forecasting of electricity spot prices using factor quantile regression averaging. International Journal of Forecasting, 32(3):957–965.Marino, A., Arango., A., Lotero, L., and Jimenez, M. (2021). Modelos de series temporales para pron´ostoco de la demanda el´ectrica del sector de explotaci´on de minas y canteras en Colombia. Revista EIA, 18:77 – 99.Masarotto, G. (1990). Bootstrap prediction intervals for autoregressions. International Journal of Forecasting, 6(2):229–239.Misiorek, A., Trueck, S., and Weron, R. (2006). Point and interval forecasting of spot electricity prices: Linear vs. non-linear time series models. Studies in Nonlinear Dynamics Econometrics, 10(3):1–34.Mohammed, A. and Kora, R. (2023). A comprehensive review on ensemble deep learning: Opportunities and challenges. Journal of King Saud University - Computer and Information Sciences, 35(2):757–774.Nielsen, D. (2016). Tree boosting with xgboost-why does xgboost win every machine learning competition. Master’s thesis, Norwegian University of Science and Technology.Ning, Y., Zhao, R., Wang, S., Yuan, B., Wang, Y., and Zheng, D. (2022). Probabilistic short-term power load forecasting based on b-scn. Energy Reports, 8:646–655. The 2022 International Conference on Energy Storage Technology and Power Systems.Nowotarski, J. and Weron, R. (2013). Computing electricity spot price prediction intervals using quantile regression and forecast averaging. HSC Research Reports HSC/13/12, Hugo Steinhaus Center, Wroclaw University of Technology.Nowotarski, J. and Weron, R. (2018). Recent advances in electricity price forecasting: A review of probabilistic forecasting. Renewable and Sustainable Energy Reviews, 81:1548– 1568.Panagiotelis, A. and Smith, M. (2008). Bayesian density forecasting of intraday electricity prices using multivariate skew t distributions. International Journal of Forecasting, 24(4):710–727.Pascual, L., Romo, J., and Ruiz, E. (2005). Bootstrap prediction intervals for powertransformed time series. International Journal of Forecasting, 21:219–235.Ramachandran, P., Zoph, B., and Le, Q. V. (2017). Searching for activation functions. retrieved from https://arxiv.org/abs/1710.05941.Son, N., Yang, S., and Na, J. (2020). Deep neural network and long short-term memory for electric power load forecasting. Applied Sciences, 10(18):6489.Stankeviciute, K., M. Alaa, A., and van der Schaar, M. (2021). Conformal time-series forecasting. In Ranzato, M., Beygelzimer, A., Dauphin, Y., Liang, P., and Vaughan, J. W., editors, Advances in Neural Information Processing Systems, volume 34, pages : 6216–6228. Curran Associates, Inc.Ullah, F. U. M., Ullah, A., Khan, N., Lee, M. Y., Rho, S., Baik, S. W., and Bai, X. (2022). 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Journal of the Royal Statistical Society: Series B (Statistical Methodology), 67(2):301– 320.EstudiantesInvestigadoresMaestrosLICENSElicense.txtlicense.txttext/plain; charset=utf-85879https://repositorio.unal.edu.co/bitstream/unal/85944/1/license.txteb34b1cf90b7e1103fc9dfd26be24b4aMD51ORIGINAL1123514281.2024.pdf1123514281.2024.pdfTesis de Maestría en Ciencias - Estadísticaapplication/pdf8700556https://repositorio.unal.edu.co/bitstream/unal/85944/2/1123514281.2024.pdfc632c03c2dce4c836fca12e4d45adc02MD52THUMBNAIL1123514281.2024.pdf.jpg1123514281.2024.pdf.jpgGenerated Thumbnailimage/jpeg4177https://repositorio.unal.edu.co/bitstream/unal/85944/3/1123514281.2024.pdf.jpg6eee7ee4ce0aef48e3056fd4cf0b5260MD53unal/85944oai:repositorio.unal.edu.co:unal/859442024-04-18 23:29:10.426Repositorio Institucional Universidad Nacional de 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Probabilistic forecasting of electricity demand in Colombia

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