Electrical consumption patterns through machine learning
Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called "Smart Metering" has been adopted, giving way to new types of meters that interact in an in...
- Autores:
-
amelec, viloria
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:11323/5948
- Acceso en línea:
- http://hdl.handle.net/11323/5948
https://repositorio.cuc.edu.co/
- Palabra clave:
- Electrical consumption
Machine learning
Smart metering
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Electrical consumption patterns through machine learning |
| title |
Electrical consumption patterns through machine learning |
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Electrical consumption patterns through machine learning Electrical consumption Machine learning Smart metering |
| title_short |
Electrical consumption patterns through machine learning |
| title_full |
Electrical consumption patterns through machine learning |
| title_fullStr |
Electrical consumption patterns through machine learning |
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Electrical consumption patterns through machine learning |
| title_sort |
Electrical consumption patterns through machine learning |
| dc.creator.fl_str_mv |
amelec, viloria Senior Naveda, Alexa Hernández Palma, Hugo Niebles Núñez, William Niebles Nuñez, Leonardo David |
| dc.contributor.author.spa.fl_str_mv |
amelec, viloria Senior Naveda, Alexa Hernández Palma, Hugo Niebles Núñez, William Niebles Nuñez, Leonardo David |
| dc.subject.spa.fl_str_mv |
Electrical consumption Machine learning Smart metering |
| topic |
Electrical consumption Machine learning Smart metering |
| description |
Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called "Smart Metering" has been adopted, giving way to new types of meters that interact in an interconnected system. This will allow to make data analysis, accurate forecasts and detecting consumption patterns that will be relevant for the decision-making process. This research focuses on discovering common patterns among customers from data collected by smart meters. |
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2020 |
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2020-01-30T13:42:22Z |
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2020-01-30T13:42:22Z |
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2020 |
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http://hdl.handle.net/11323/5948 |
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Corporación Universidad de la Costa |
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eng |
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eng |
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10.1088/1742-6596/1432/1/012093/pdf |
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[1] 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). [2] Joana M. Abreu, Francisco Camara Pereira, Paulo Ferrao, using pattern recognition to identify habitual behavior in residential electricity consumption, Energy and Buildings, Vol. 49, June 2012, pp. 479-487, ELSEVIER [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] Leo Breiman, Random Forests, Machine Learning, Vol. 45, Issue 1, October 2001, pp. 5- 32, Springer. [8] A.S.Ahmad, et al., A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews, Vol. 33, May 2014, pp. 102–109 [9] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016). [10] Clustering – EcuRed. Disponible vía web en http://www.ecured.cu/Clustering. Revisado por última vez el 29 de marzo de 2017. [11] 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. [12] 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. [13] 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. [14] 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 [15] 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. |
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amelec, viloriaSenior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamNiebles Nuñez, Leonardo David2020-01-30T13:42:22Z2020-01-30T13:42:22Z20201742-65881742-6596http://hdl.handle.net/11323/5948Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called "Smart Metering" has been adopted, giving way to new types of meters that interact in an interconnected system. This will allow to make data analysis, accurate forecasts and detecting consumption patterns that will be relevant for the decision-making process. This research focuses on discovering common patterns among customers from data collected by smart meters.amelec, viloria-will be generated-orcid-0000-0003-2673-6350-600Senior Naveda, AlexaHernández Palma, HugoNiebles Núñez, WilliamNiebles Nuñez, Leonardo David-will be generated-orcid-0000-0003-2970-2498-600engJournal of Physics: Conference Series10.1088/1742-6596/1432/1/012093/pdf[1] 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).[2] Joana M. Abreu, Francisco Camara Pereira, Paulo Ferrao, using pattern recognition to identify habitual behavior in residential electricity consumption, Energy and Buildings, Vol. 49, June 2012, pp. 479-487, ELSEVIER[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] Leo Breiman, Random Forests, Machine Learning, Vol. 45, Issue 1, October 2001, pp. 5- 32, Springer.[8] A.S.Ahmad, et al., A review on applications of ANN and SVM for building electrical energy consumption forecasting, Renewable and Sustainable Energy Reviews, Vol. 33, May 2014, pp. 102–109[9] Witten, I.; Frank, E.; Hall, M.; Pal, C.: Data Mining Practical Machine Learning Tools and Techniques. Elsevier 4th Ed, pp. 167-169 (2016).[10] Clustering – EcuRed. Disponible vía web en http://www.ecured.cu/Clustering. Revisado por última vez el 29 de marzo de 2017.[11] 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.[12] 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.[13] 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.[14] 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[15] 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.CC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Electrical consumptionMachine learningSmart meteringElectrical consumption patterns through machine learningArtículo 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/acceptedVersionPublicationORIGINALElectrical Consumption patterns through machine learning.pdfElectrical Consumption patterns through machine learning.pdfapplication/pdf761559https://repositorio.cuc.edu.co/bitstreams/d12815e3-2a5d-41be-9551-ca940b47eb8a/download51bcfaee96b26d0a303104ca7e418c4fMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/cc453e36-0d31-405c-b5d6-bc354aa3dd83/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-81748https://repositorio.cuc.edu.co/bitstreams/4bba21b7-7740-4196-9b63-8e19f22deee3/download8a4605be74aa9ea9d79846c1fba20a33MD53THUMBNAILElectrical Consumption patterns through machine learning.pdf.jpgElectrical Consumption patterns through machine learning.pdf.jpgimage/jpeg27191https://repositorio.cuc.edu.co/bitstreams/0aa33c54-0e31-491e-b574-12969a0b6043/download13cb72f549cadccdd5e302312f074458MD55TEXTElectrical Consumption patterns through machine learning.pdf.txtElectrical Consumption patterns through machine learning.pdf.txttext/plain18845https://repositorio.cuc.edu.co/bitstreams/ca456f49-9692-45fe-8e8b-b54bc9eaec42/download32c8f83e81e5f042595a85ccbd65b31dMD5611323/5948oai:repositorio.cuc.edu.co:11323/59482024-09-16 16:39:18.811http://creativecommons.org/publicdomain/zero/1.0/CC0 1.0 Universalopen.accesshttps://repositorio.cuc.edu.coRepositorio de la Universidad de la Costa CUCrepdigital@cuc.edu.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 |