Prediction of electric consumption using multiple linear regression methods

In the new global and local scenario, the advent of intelligent distribution networks, or Smart Grids, allows the collection of data about the operational state of the electric network in real time. Based on this data availability, the consumption prediction becomes feasible and convenient in the sh...

Full description

Autores:
Viloria, Amelec
Hernandez-P, Hugo
Pineda, Omar
Vargas, Jesús
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:11323/7782
Acceso en línea:
https://hdl.handle.net/11323/7782
https://doi.org/10.1007/978-981-15-3125-5_45
https://repositorio.cuc.edu.co/
Palabra clave:
Energy consumption
Short term load forecasting
Variable selection
Linear regression
Rights
openAccess
License
Attribution-NonCommercial-NoDerivatives 4.0 International
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oai_identifier_str oai:repositorio.cuc.edu.co:11323/7782
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Prediction of electric consumption using multiple linear regression methods
title Prediction of electric consumption using multiple linear regression methods
spellingShingle Prediction of electric consumption using multiple linear regression methods
Energy consumption
Short term load forecasting
Variable selection
Linear regression
title_short Prediction of electric consumption using multiple linear regression methods
title_full Prediction of electric consumption using multiple linear regression methods
title_fullStr Prediction of electric consumption using multiple linear regression methods
title_full_unstemmed Prediction of electric consumption using multiple linear regression methods
title_sort Prediction of electric consumption using multiple linear regression methods
dc.creator.fl_str_mv Viloria, Amelec
Hernandez-P, Hugo
Pineda, Omar
Vargas, Jesús
dc.contributor.author.spa.fl_str_mv Viloria, Amelec
Hernandez-P, Hugo
Pineda, Omar
Vargas, Jesús
dc.subject.spa.fl_str_mv Energy consumption
Short term load forecasting
Variable selection
Linear regression
topic Energy consumption
Short term load forecasting
Variable selection
Linear regression
description In the new global and local scenario, the advent of intelligent distribution networks, or Smart Grids, allows the collection of data about the operational state of the electric network in real time. Based on this data availability, the consumption prediction becomes feasible and convenient in the short term, from a few hours to a week (temporary variables). The research proposes that the method used to present the temporary variables for a system to predict electrical consumption affects the results. To verify this hypothesis, different methods for representing these variables are considered, applied to the problem of predicting daily values of electricity consumption in the city of Bogota, Colombia.
publishDate 2020
dc.date.issued.none.fl_str_mv 2020
dc.date.accessioned.none.fl_str_mv 2021-01-28T12:56:37Z
dc.date.available.none.fl_str_mv 2021-01-28T12:56:37Z
dc.type.spa.fl_str_mv Artículo de revista
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dc.type.content.spa.fl_str_mv Text
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status_str acceptedVersion
dc.identifier.uri.spa.fl_str_mv https://hdl.handle.net/11323/7782
dc.identifier.doi.spa.fl_str_mv https://doi.org/10.1007/978-981-15-3125-5_45
dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
dc.identifier.reponame.spa.fl_str_mv REDICUC - Repositorio CUC
dc.identifier.repourl.spa.fl_str_mv https://repositorio.cuc.edu.co/
url https://hdl.handle.net/11323/7782
https://doi.org/10.1007/978-981-15-3125-5_45
https://repositorio.cuc.edu.co/
identifier_str_mv Corporación Universidad de la Costa
REDICUC - Repositorio CUC
dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.references.spa.fl_str_mv 1. 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, Cham
2. 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, Switzerland, pp 618–625
3. 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, June. Springer, Cham, pp 174–185
4. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36:1627–1637 (Preprint)
5. Xue Y, Lai Y (2016) The integration of great energy thinking and big datas thinking: big data and electricity big data. Power Syst Autom 40(1):1–8
6. Wang Y, Chen Q, Kang C et al (2017) Clustering of electricity consumption behaviour dynamics toward big data applications. IEEE Trans Smart Grid 7(5):2437–2447
7. Liu R, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, China
8. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–966
9. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN: 8420540250
10. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison
11. Andersson M (2009) A comparison of nine PLS1 algorithms. J Chemometr 23:518–529
12. Xu Q-S, Liang Y-Z (2001) Monte Carlo cross validation. Chemometr Intell Lab 56:1–11
13. Li H-D, Xu Q-S, Liang Y-Z (2017) A phase diagram for gene selection and disease classification. Chemometr Intell Lab 167:208–213
14. Cao DS, Liang YZ, Xu QS, Li HD, Chen X (2010) A new strategy of outlier detection for QSAR/QSPR. J Comput Chem 31:592–602
15. Tian W (2017) The research into methods of map building and path planning on mobile robots. In: IEEE 2nd information technology, networking, electronic and automation control conference (ITNEC 2017), pp 1087–1090
16. Dugas M et al (2014) Missing semantic annotation in databases. Methods Inf Med 53(6):516–517
17. Silberschatz A, Korth HF, Sudarshan S et al (1997) Database system concepts, vol 4. McGraw-Hill, New York
dc.rights.spa.fl_str_mv Attribution-NonCommercial-NoDerivatives 4.0 International
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dc.source.spa.fl_str_mv Lecture Notes in Electrical Engineering
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spelling Viloria, AmelecHernandez-P, HugoPineda, OmarVargas, Jesús2021-01-28T12:56:37Z2021-01-28T12:56:37Z2020https://hdl.handle.net/11323/7782https://doi.org/10.1007/978-981-15-3125-5_45Corporación 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 the collection of data about the operational state of the electric network in real time. Based on this data availability, the consumption prediction becomes feasible and convenient in the short term, from a few hours to a week (temporary variables). The research proposes that the method used to present the temporary variables for a system to predict electrical consumption affects the results. To verify this hypothesis, different methods for representing these variables are considered, applied to the problem of predicting daily values of electricity consumption in the city of Bogota, Colombia.Viloria, AmelecHernandez-P, HugoPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600Vargas, Jesúsapplication/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/10.1007/978-981-15-3125-5_45Energy consumptionShort term load forecastingVariable selectionLinear regressionPrediction of electric consumption using multiple linear regression methodsArtículo 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. 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, Cham2. 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, Switzerland, pp 618–6253. 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, June. Springer, Cham, pp 174–1854. Perez R, Vásquez C, Viloria A (2019) An intelligent strategy for faults location in distribution networks with distributed generation. J Intell Fuzzy Syst 36:1627–1637 (Preprint)5. Xue Y, Lai Y (2016) The integration of great energy thinking and big datas thinking: big data and electricity big data. Power Syst Autom 40(1):1–86. Wang Y, Chen Q, Kang C et al (2017) Clustering of electricity consumption behaviour dynamics toward big data applications. IEEE Trans Smart Grid 7(5):2437–24477. Liu R, Ding W (2011) Statistical analysis and application of SAS. China Machine Press, China8. Ozger M, Cetinkaya O, Akan OB (2017) Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob Netw Appl 23(4):956–9669. Isasi P, Galván I (2004) Redes de Neuronas Artificiales. Un enfoque Práctico. Pearson, London. ISBN: 842054025010. Mangasarian O (1997) Arbitrary-norm separating plane. Technical report 97-07, Computer Science Dept., Univ. Wisconsin Madison11. Andersson M (2009) A comparison of nine PLS1 algorithms. J Chemometr 23:518–52912. Xu Q-S, Liang Y-Z (2001) Monte Carlo cross validation. Chemometr Intell Lab 56:1–1113. Li H-D, Xu Q-S, Liang Y-Z (2017) A phase diagram for gene selection and disease classification. Chemometr Intell Lab 167:208–21314. Cao DS, Liang YZ, Xu QS, Li HD, Chen X (2010) A new strategy of outlier detection for QSAR/QSPR. J Comput Chem 31:592–60215. Tian W (2017) The research into methods of map building and path planning on mobile robots. In: IEEE 2nd information technology, networking, electronic and automation control conference (ITNEC 2017), pp 1087–109016. Dugas M et al (2014) Missing semantic annotation in databases. Methods Inf Med 53(6):516–51717. Silberschatz A, Korth HF, Sudarshan S et al (1997) Database system concepts, vol 4. 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