Electric consumption pattern from big data
From the concept of smart grid, reaching an efficient and reliable network is a task that implies several stages and sub-stages with a defined and specific mission. In this way, the intelligent measurement stage conformed by the smart meters obtains the information of electrical consumption from the...
- Autores:
-
Viloria, Amelec
Hernandez-P, Hugo
Pineda, Omar
Diago Orozco, Victoria
- 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:11323/7730
- Acceso en línea:
- https://hdl.handle.net/11323/7730
https://doi.org/10.1007/978-981-15-3125-5_47
https://repositorio.cuc.edu.co/
- Palabra clave:
- Big data, MapReduce
Meter data management system (MDMS)
Smart grid
Smart metering
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Electric consumption pattern from big data |
| title |
Electric consumption pattern from big data |
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Electric consumption pattern from big data Big data, MapReduce Meter data management system (MDMS) Smart grid Smart metering |
| title_short |
Electric consumption pattern from big data |
| title_full |
Electric consumption pattern from big data |
| title_fullStr |
Electric consumption pattern from big data |
| title_full_unstemmed |
Electric consumption pattern from big data |
| title_sort |
Electric consumption pattern from big data |
| dc.creator.fl_str_mv |
Viloria, Amelec Hernandez-P, Hugo Pineda, Omar Diago Orozco, Victoria |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Hernandez-P, Hugo Pineda, Omar Diago Orozco, Victoria |
| dc.subject.spa.fl_str_mv |
Big data, MapReduce Meter data management system (MDMS) Smart grid Smart metering |
| topic |
Big data, MapReduce Meter data management system (MDMS) Smart grid Smart metering |
| description |
From the concept of smart grid, reaching an efficient and reliable network is a task that implies several stages and sub-stages with a defined and specific mission. In this way, the intelligent measurement stage conformed by the smart meters obtains the information of electrical consumption from the users or consumers (residential, commercial, and industrial). For this purpose, a smart metering infrastructure made of wireless telecommunications and fiber optic has been generated allows to guarantee the connectivity of the smart meters and the central office of electric companies. This paper aims to describe the use of MapReduce as a technique to obtain information about the load curve at an appropriate time to obtain trends and statistics related to the pattern of residential electricity consumption. |
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2020 |
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2020 |
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2021-01-20T18:40:04Z |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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eng |
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eng |
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1. 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, Berlin 2. Perez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International conference on sensing and imaging. Springer, Berlin, pp 174–185 3. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 1–11 4. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted Gaussian algorithm means. Stat Probab Lett 137:148–156. 5. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure MA, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–73 6. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) Inice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci. 7. Sánchez L, Vásquez C, Viloria A, Rodríguez Potes L (2018) Greenhouse gases emissions and electric power generation in Latin American Countries in the Period 2006–2013. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Berlin 8. Sun M, Konstantelos I, Strbac G (2017) C-vine copula mixture model for clustering of residential electrical load pattern data. Power Syst IEEE Trans On 32(3):2382–2393 9. Perez R et al (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Berlin 10. Silva V, Jesús A (2013) Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced materials research, vol 601. Trans Tech Publications, pp 618–625 11. Kim M, Park S, Han K, Kim N, Kyun Choi J (2018) Dynamics of electricity consumers for classifying power consumption data using PCA. In: Big data and smart computing (BigComp) 2018 IEEE international conference on, pp 697–700 12. Deng ZH, Yu H, Yang Y (2016) Identifying sentiment words using an optimization model with L1, regularization. In: Thirtieth AAAI conference on artificial intelligence. AAAI Press pp 115–121 13. Alshareef SM, Morsi WG (2017) Probabilistic commercial load profiles at different climate zones. In: Electrical power and energy conference (EPEC) 2017 IEEE, pp 1–7 14. Gao XZ, Kaarna A, Lensu L, Honkapuro S (2017) A hybrid method for short-term electricity consumption prediction. In: Industrial electronics society IECON 2017—43rd annual conference of the IEEE, pp 7393–7398 15. Chen T, Qian K, Mutanen A, Schuller B, Järventausta P, Su W (2017) Classification of electricity customer groups towards individualized price scheme design. In: Power symposium (NAPS) 2017 North American, pp 1–4 16. Lv Z, Han Z, Yang L, Gang W (2017) An improved CFSFDP algorithm with cluster center automatically selected based on weighted average method. In: 2017 IEEE 7th annual international conference on CYBER technology in automation control and intelligent systems (CYBER), pp 955–959 17. Adiyoso W, Krisnadhi A, Wibisono A, Purbarani SC, Saraswati AD, Putri AFR, Saladdin IR, Anwar SRC (2018) Time performance analysis of multi-CPU and multi-GPU in big data clustering computation. In: 2018 international workshop on big data and information security (IWBIS), pp 113–116 |
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Viloria, AmelecHernandez-P, HugoPineda, OmarDiago Orozco, Victoria2021-01-20T18:40:04Z2021-01-20T18:40:04Z2020https://hdl.handle.net/11323/7730https://doi.org/10.1007/978-981-15-3125-5_47Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/From the concept of smart grid, reaching an efficient and reliable network is a task that implies several stages and sub-stages with a defined and specific mission. In this way, the intelligent measurement stage conformed by the smart meters obtains the information of electrical consumption from the users or consumers (residential, commercial, and industrial). For this purpose, a smart metering infrastructure made of wireless telecommunications and fiber optic has been generated allows to guarantee the connectivity of the smart meters and the central office of electric companies. This paper aims to describe the use of MapReduce as a technique to obtain information about the load curve at an appropriate time to obtain trends and statistics related to the pattern of residential electricity consumption.Viloria, AmelecHernandez-P, HugoPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Diago Orozco, Victoriaapplication/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007%2F978-981-15-3125-5_47Big data, MapReduceMeter data management system (MDMS)Smart gridSmart meteringElectric consumption pattern from big dataArtículo 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/acceptedVersion1. 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, Berlin2. Perez R, Inga E, Aguila A, Vásquez C, Lima L, Viloria A, Henry MA (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: International conference on sensing and imaging. Springer, Berlin, pp 174–1853. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 1–114. Chakraborty S, Das S (2018) Simultaneous variable weighting and determining the number of clusters—a weighted Gaussian algorithm means. Stat Probab Lett 137:148–156.5. Bontempi G, Ben Taieb S, Borgne YA (2013) Machine learning strategies for time series forecasting. In: Aufaure MA, Zimányi E (eds) Lecture notes in business information processing, vol 138, no 1. Springer, Heidelberg, pp 70–736. Abdul Masud M, Zhexue Huang J, Wei C, Wang J, Khan I, Zhong M (2018) Inice: a new approach for identifying the number of clusters and initial cluster centres. Inf Sci.7. Sánchez L, Vásquez C, Viloria A, Rodríguez Potes L (2018) Greenhouse gases emissions and electric power generation in Latin American Countries in the Period 2006–2013. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Berlin8. Sun M, Konstantelos I, Strbac G (2017) C-vine copula mixture model for clustering of residential electrical load pattern data. Power Syst IEEE Trans On 32(3):2382–23939. Perez R et al (2018) Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan Y, Shi Y, Tang Q (eds) Advances in swarm intelligence. ICSI 2018. Lecture notes in computer science, vol 10942. Springer, Berlin10. Silva V, Jesús A (2013) Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced materials research, vol 601. Trans Tech Publications, pp 618–62511. Kim M, Park S, Han K, Kim N, Kyun Choi J (2018) Dynamics of electricity consumers for classifying power consumption data using PCA. In: Big data and smart computing (BigComp) 2018 IEEE international conference on, pp 697–70012. Deng ZH, Yu H, Yang Y (2016) Identifying sentiment words using an optimization model with L1, regularization. In: Thirtieth AAAI conference on artificial intelligence. AAAI Press pp 115–12113. Alshareef SM, Morsi WG (2017) Probabilistic commercial load profiles at different climate zones. In: Electrical power and energy conference (EPEC) 2017 IEEE, pp 1–714. Gao XZ, Kaarna A, Lensu L, Honkapuro S (2017) A hybrid method for short-term electricity consumption prediction. In: Industrial electronics society IECON 2017—43rd annual conference of the IEEE, pp 7393–739815. Chen T, Qian K, Mutanen A, Schuller B, Järventausta P, Su W (2017) Classification of electricity customer groups towards individualized price scheme design. In: Power symposium (NAPS) 2017 North American, pp 1–416. Lv Z, Han Z, Yang L, Gang W (2017) An improved CFSFDP algorithm with cluster center automatically selected based on weighted average method. In: 2017 IEEE 7th annual international conference on CYBER technology in automation control and intelligent systems (CYBER), pp 955–95917. Adiyoso W, Krisnadhi A, Wibisono A, Purbarani SC, Saraswati AD, Putri AFR, Saladdin IR, Anwar SRC (2018) Time performance analysis of multi-CPU and multi-GPU in big data clustering computation. In: 2018 international workshop on big data and information security (IWBIS), pp 113–116PublicationORIGINALElectric consumption pattern from big data.pdfElectric consumption pattern from big data.pdfapplication/pdf93029https://repositorio.cuc.edu.co/bitstreams/4c778c9d-675c-41ee-81f5-1c6a90ca8496/download44d3c606d84ff06a9044063011ebcd5aMD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8805https://repositorio.cuc.edu.co/bitstreams/53c0cd80-2849-4f5d-b8ca-ab9dce0e99cc/download4460e5956bc1d1639be9ae6146a50347MD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/3a5303b4-bb2d-4650-97c3-92601b6b5feb/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILElectric consumption pattern from big data.pdf.jpgElectric consumption pattern from big data.pdf.jpgimage/jpeg29471https://repositorio.cuc.edu.co/bitstreams/47fbbc29-c044-4176-bad1-44fe40478ddc/downloadcd67c8ba3615f2229700620bf2520cf3MD54TEXTElectric consumption pattern from 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