Temporary variables for predicting electricity consumption through data mining

In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a...

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Autores:
silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Niebles Nuñez, Leonardo David
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:11323/5947
Acceso en línea:
https://hdl.handle.net/11323/5947
https://repositorio.cuc.edu.co/
Palabra clave:
Electricity
Temporary Variables
Data mining
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_034040c80604bfdc45e7bf6bc44766c6
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5947
network_acronym_str RCUC2
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repository_id_str
dc.title.spa.fl_str_mv Temporary variables for predicting electricity consumption through data mining
title Temporary variables for predicting electricity consumption through data mining
spellingShingle Temporary variables for predicting electricity consumption through data mining
Electricity
Temporary Variables
Data mining
title_short Temporary variables for predicting electricity consumption through data mining
title_full Temporary variables for predicting electricity consumption through data mining
title_fullStr Temporary variables for predicting electricity consumption through data mining
title_full_unstemmed Temporary variables for predicting electricity consumption through data mining
title_sort Temporary variables for predicting electricity consumption through data mining
dc.creator.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Niebles Nuñez, Leonardo David
dc.contributor.author.spa.fl_str_mv silva d, jesus g
Senior Naveda, Alexa
Hernández Palma, Hugo
Niebles Núñez, William
Niebles Nuñez, Leonardo David
dc.subject.spa.fl_str_mv Electricity
Temporary Variables
Data mining
topic Electricity
Temporary Variables
Data mining
description In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-01-30T13:38:21Z
dc.date.available.none.fl_str_mv 2020-01-30T13:38:21Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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identifier_str_mv 1742-6596
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv 10.1088/1742-6596/1432/1/012033/pdf
dc.relation.references.spa.fl_str_mv [1] R. Sevlian and R. Rajagopal, “Short Term Electricity Load Forecasting on Varying Levels of Aggregation,” ArXivPrepr.ArXiv14040058, 2014.
[2] Z. Y. Wang, C. X. Guo, and Y. J. Cao, “A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network,” in Power Engineering Conference, 2005. IPEC 2005. The 7th International, 2005, pp. 1–173.
[3] I. Drezga and S. Rahman, “Input variable selection for ANN-based short-term load forecasting,” IEEE Trans. Power Syst., vol. 13, no. 4, pp. 1238–1244, Nov. 1998.
[4] Y.-C. Guo, “An integrated PSO for parameter determination and feature selection of SVR and its application in STLF,” in 2009 International Conference on Machine Learning and Cybernetics, 2009, vol. 1, pp. 359–364.
[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] 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.
[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] 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
[17] 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.
[18] 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).
[19] 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/
[20] 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).
[21] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. IEEE Transactions on Image Processing, 20, 293-298.
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spelling silva d, jesus gSenior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamNiebles Nuñez, Leonardo David2020-01-30T13:38:21Z2020-01-30T13:38:21Z20201742-65961742-6588https://hdl.handle.net/11323/5947Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/In the new global and local scenario, the advent of intelligent distribution networks or Smart Grids allows real-time collection of data on the operating status of the electricity grid. Based on this availability of data, it is feasible and convenient to predict consumption in the short term, from a few hours to a week. The hypothesis of the study is that the method used to present time variables to a prediction system of electricity consumption affects the results.silva d, jesus g-will be generated-orcid-0000-0003-3555-9149-600Senior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamNiebles Nuñez, Leonardo David-will be generated-orcid-0000-0003-2970-2498-600engJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012033/pdf[1] R. Sevlian and R. Rajagopal, “Short Term Electricity Load Forecasting on Varying Levels of Aggregation,” ArXivPrepr.ArXiv14040058, 2014.[2] Z. Y. Wang, C. X. Guo, and Y. J. Cao, “A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network,” in Power Engineering Conference, 2005. IPEC 2005. The 7th International, 2005, pp. 1–173.[3] I. Drezga and S. Rahman, “Input variable selection for ANN-based short-term load forecasting,” IEEE Trans. Power Syst., vol. 13, no. 4, pp. 1238–1244, Nov. 1998.[4] Y.-C. Guo, “An integrated PSO for parameter determination and feature selection of SVR and its application in STLF,” in 2009 International Conference on Machine Learning and Cybernetics, 2009, vol. 1, pp. 359–364.[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] 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.[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] 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[17] 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.[18] 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).[19] 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/[20] 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).[21] Khelifi, F. J., J. (2011). K-NN Regression to Improve Statistical Feature Extraction for Texture Retrieval. 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