Forecasting electric load demand through advanced statistical techniques

Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their a...

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Autores:
silva d, jesus g
Senior Naveda, Alexa
García Guiliany, Jesús Enrique
Niebles Nuñez, William
Hernández Palma, Hugo
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5960
Acceso en línea:
https://hdl.handle.net/11323/5960
https://repositorio.cuc.edu.co/
Palabra clave:
Electric charge
Electrical demand
Forecasting models
Rights
openAccess
License
CC0 1.0 Universal
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/5960
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repository_id_str
dc.title.spa.fl_str_mv Forecasting electric load demand through advanced statistical techniques
title Forecasting electric load demand through advanced statistical techniques
spellingShingle Forecasting electric load demand through advanced statistical techniques
Electric charge
Electrical demand
Forecasting models
title_short Forecasting electric load demand through advanced statistical techniques
title_full Forecasting electric load demand through advanced statistical techniques
title_fullStr Forecasting electric load demand through advanced statistical techniques
title_full_unstemmed Forecasting electric load demand through advanced statistical techniques
title_sort Forecasting electric load demand through advanced statistical techniques
dc.creator.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
García Guiliany, Jesús Enrique
Niebles Nuñez, William
Hernández Palma, Hugo
dc.contributor.author.spa.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
García Guiliany, Jesús Enrique
Niebles Nuñez, William
Hernández Palma, Hugo
dc.subject.spa.fl_str_mv Electric charge
Electrical demand
Forecasting models
topic Electric charge
Electrical demand
Forecasting models
description Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-01-30T13:48:13Z
dc.date.available.none.fl_str_mv 2020-01-30T13:48:13Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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1742-6596
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identifier_str_mv 1742-6588
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Corporación Universidad de la Costa
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv 10.1088/1742-6596/1432/1/012031/pdf
dc.relation.references.spa.fl_str_mv [1] Castellanos Domíngez, M. I., Quevedo Castro, C. M., Vega Ramírez, A., Grangel González, I., & Moreno Rodríguez, R. (2016). Sistema basado en ontología para el apoyo a la toma de decisiones en el proceso de gestión ambiental empresarial. Paper presented at the II International Workshop of Semantic Web, La Habana, Cuba. http://ceur-ws.org/Vol-1797/
[2] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).
[3] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).
[4] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.
[5] Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034
[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)
[7] IHOBE. (1999). Guía de Indicadores Medioambientales para la Empresa. Berlin: Ministerio Federal para el Medio Ambiente, la Conservación de la Naturaleza y la Seguridad Nuclear.
[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)
[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)
[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).
[11] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognition
[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.
[13] Sanchez L., Vásquez C., Viloria A., Cmeza-estrada (2018) Conglomerates of Latin American Countries and Public Policies for the Sustainable Development of the Electric Power Generation Sector. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.
[14] Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., & Henry, M. A. (2018, June). Fault diagnosis on electrical distribution systems based on fuzzy logic. In International Conference on Sensing and Imaging (pp. 174-185). Springer, Cham.
[15] Perez, Ramón, Carmen Vásquez, and Amelec Viloria. "An intelligent strategy for faults location in distribution networks with distributed generation." Journal of Intelligent & Fuzzy Systems Preprint (2019): 1-11.
[16] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.
[17] Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian algorithm means. Stat. Probab. Lett. 137, 148–156 (2018). https://doi.org/10.1016/j.spl.2018.01.015.
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dc.publisher.spa.fl_str_mv Journal of Physics: Conference Series
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spelling silva d, jesus gSenior Naveda, AlexaGarcía Guiliany, Jesús EnriqueNiebles Nuñez, WilliamHernández Palma, Hugo2020-01-30T13:48:13Z2020-01-30T13:48:13Z20201742-65881742-6596https://hdl.handle.net/11323/5960Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Traditional forecasting models have been widely used for decision-making in production, finance and energy. Such is the case of the ARIMA models, developed in the 1970s by George Box and Gwilym Jenkins [1], which incorporate characteristics of the past models of the same series, according to their autocorrelation. This work compares advanced statistical methods for determining the demand for electricity in Colombia, including the SARIMA, econometric and Bayesian methods.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-600Senior Naveda, AlexaGarcía Guiliany, Jesús Enrique-will be generated-orcid-0000-0003-3777-3667-600Niebles Nuñez, WilliamHernández Palma, HugoengJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012031/pdf[1] Castellanos Domíngez, M. I., Quevedo Castro, C. M., Vega Ramírez, A., Grangel González, I., & Moreno Rodríguez, R. (2016). Sistema basado en ontología para el apoyo a la toma de decisiones en el proceso de gestión ambiental empresarial. Paper presented at the II International Workshop of Semantic Web, La Habana, Cuba. http://ceur-ws.org/Vol-1797/[2] Pretnar, A. The Mystery of Test & Score. Ljubljana: University of Ljubljana. Retrieved from: https://orange.biolab.si/blog/2019/1/28/the-mystery-of-test-and-score/ (2019).[3] Yasser, A. M., Clawson, K., & Bowerman, C.: Saving cultural heritage with digital make-believe: machine learning and digital techniques to the rescue. In Proceedings of the 31st British Computer Society Human Computer Interaction Conference (p. 97). BCS Learning & Development Ltd. (2017).[4] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.[5] Abdul Masud, M., Zhexue Huang, J., Wei, C., Wang, J., Khan, I., Zhong, M.: Inice: A New Approach for Identifying the Number of Clusters and Initial Cluster Centres. Inf. Sci. (2018). https://doi.org/10.1016/j.ins.2018.07.034[6] Martins, L.; Carvalho, R.; Victorino, C.; Holanda, M.: Early Prediction of College Attrition Using Data Mining. 16th IEEE International Conference on Machine Learning and Applications (ICMLA), pp. 1075-1078 (2017)[7] IHOBE. (1999). Guía de Indicadores Medioambientales para la Empresa. Berlin: Ministerio Federal para el Medio Ambiente, la Conservación de la Naturaleza y la Seguridad Nuclear.[8] Russell, S.; Norvig, P.: Artificial Intelligence A Modern Approach. Pearson Education 3rd Ed, pp. 705 (2010)[9] Makhabel, B.: Learning Data Mining with R. Packt Publishing 1st Ed, pp. 143 (2015)[10] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).[11] Bishop, C. (1995). Extremely well-written, up-to-date. Requires a good mathematical background, but rewards careful reading, putting neural networks firmly into a statistical context. Neural Networks for Pattern Recognition[12] Gaitán-Angulo, M., Viloria, A., & Abril, J. E. S. (2018, June). Hierarchical Ascending Classification: An Application to Contraband Apprehensions in Colombia (2015–2016). In Data Mining and Big Data: Third International Conference, DMBD 2018, Shanghai, China, June 17– 22, 2018, Proceedings (Vol. 10943, p. 168). Springer.[13] Sanchez L., Vásquez C., Viloria A., Cmeza-estrada (2018) Conglomerates of Latin American Countries and Public Policies for the Sustainable Development of the Electric Power Generation Sector. In: Tan Y., Shi Y., Tang Q. (eds) Data Mining and Big Data. DMBD 2018. Lecture Notes in Computer Science, vol 10943. Springer, Cham.[14] Perez, R., Inga, E., Aguila, A., Vásquez, C., Lima, L., Viloria, A., & Henry, M. A. (2018, June). Fault diagnosis on electrical distribution systems based on fuzzy logic. In International Conference on Sensing and Imaging (pp. 174-185). Springer, Cham.[15] Perez, Ramón, Carmen Vásquez, and Amelec Viloria. "An intelligent strategy for faults location in distribution networks with distributed generation." Journal of Intelligent & Fuzzy Systems Preprint (2019): 1-11.[16] Bucci, N., Luna, M., Viloria, A., García, J. H., Parody, A., Varela, N., & López, L. A. B. (2018, June). Factor analysis of the psychosocial risk assessment instrument. In International Conference on Data Mining and Big Data (pp. 149-158). Springer, Cham.[17] Chakraborty, S., Das, S.: Simultaneous variable weighting and determining the number of clusters—A weighted Gaussian algorithm means. Stat. Probab. Lett. 137, 148–156 (2018). https://doi.org/10.1016/j.spl.2018.01.015.CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Electric chargeElectrical demandForecasting modelsForecasting electric load demand through advanced statistical techniquesArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersionPublicationORIGINALForecasting electric load demand through advanced statistical techniques.pdfForecasting electric load demand through advanced statistical techniques.pdfapplication/pdf696799https://repositorio.cuc.edu.co/bitstreams/b93be234-67e3-4458-86ae-44267d089477/downloadf2bea7859673140e91ef05d829c81b72MD51Forecasting Electric Load Demand through Advanced Statistical.pdfForecasting Electric Load Demand through Advanced Statistical.pdfapplication/pdf1553110https://repositorio.cuc.edu.co/bitstreams/2713dae5-5517-4495-9332-f23525800ed8/downloadb0149f1a0739e3fc527fbfd78c2cfca1MD56CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; 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