Mostrar el registro sencillo del ítem

Analyzing and predicting power consumption profiles using big data

dc.contributor.authoramelec, viloriaspa
dc.contributor.authorPrieto Pulido, Ronald Antoniospa
dc.contributor.authorGarcía Guiliany, Jesússpa
dc.contributor.authorMartínez Ventura, Jairospa
dc.contributor.authorHernández Palma, Hugospa
dc.contributor.authorJinete Torres, Joséspa
dc.contributor.authorREDONDO BILBAO, OSMAN ENRIQUEspa
dc.contributor.authorPineda Lezam, Omar Bonergespa
dc.date.accessioned2020-01-15T19:30:28Z
dc.date.available2020-01-15T19:30:28Z
dc.date.issued2019
dc.identifier.issn18650929spa
dc.identifier.urihttp://hdl.handle.net/11323/5828spa
dc.description.abstractThe Euclidean distance (ED), the mean absolute error (MAE), the mean absolute percentage error (MAPE) and the root of the mean quadratic error (RMQE) are used to evaluate the predictive capability of the models supported by each statistical method, asserting, according to the assessment, that the best predictions come from the ARIMA method. This paper presents a prediction study for two buildings located at the University of Mumbai in India, in order to determine a method that fits the forecasts of organization expensesspa
dc.description.abstractLa distancia euclidiana (DE), el error absoluto medio (MAE), el error porcentual absoluto medio (MAPE) y la raíz del error cuadrático medio (RMQE) se utilizan para evaluar la capacidad predictiva de los modelos soportados por cada método estadístico, afirmando, según la evaluación, que las mejores predicciones provienen del método ARIMA. Este documento presenta un estudio de predicción para dos edificios ubicados en la Universidad de Mumbai en India, con el fin de determinar un método que se ajuste a las previsiones de gastos de la organización.spa
dc.description.abstractPérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005)spa
dc.language.isoeng
dc.publisherCommunications in Computer and Information Sciencespa
dc.relation.ispartofhttps://doi.org/10.1007/978-981-15-1304-6_31spa
dc.rightsCC0 1.0 Universalspa
dc.rights.urihttp://creativecommons.org/publicdomain/zero/1.0/spa
dc.subjectPredictionspa
dc.subjectPower consumptionspa
dc.subjectBig Dataspa
dc.subjectARIMAspa
dc.subjectPredicciónspa
dc.subjectConsumo de energíaspa
dc.titleAnalyzing and predicting power consumption profiles using big dataspa
dc.typeArtículo de revistaspa
dc.rights.accessrightsinfo:eu-repo/semantics/openAccessspa
dc.identifier.instnameCorporación Universidad de la Costaspa
dc.identifier.reponameREDICUC - Repositorio CUCspa
dc.identifier.repourlhttps://repositorio.cuc.edu.co/spa
dc.relation.referencesBradley, P., Mangasarian, O.: Feature selection via concave minimization and support vector machines. In: Shavlik, J. (ed.) Machine Learning, pp. 82–90. ICML, San Francisco (1998)spa
dc.relation.referencesHu, C., Du, S., Su, J., et al.: Discussion on the ways of purchasing and selling electricity and the mode of operation in China’s electricity sales companies under the background of new electric power reform. Power Netw. Technol. 40(11), 3293–3299 (2016)spa
dc.relation.referencesXue, Y., Lai, Y.: The integration of great energy thinking and big datas thinking: Big data and electricity big data. Power Syst. Autom. 40(1), 1–8 (2016)spa
dc.relation.referencesWang, Y., Chen, Q., Kang, C., et al.: Clustering of electricity consumption behaviour dynamics toward big data applications. IEEE Trans. Smart Grid 7(5), 2437–2447 (2017)spa
dc.relation.referencesRong, L., Guosheng, F., Weidai, D.: Statistical Analysis and Application of SAS. China Machine Press, Beijing (2011)spa
dc.relation.references. Ozger, M., Cetinkaya, O., Akan, O.B.: Energy harvesting cognitive radio networking for IoT-enabled smart grid. Mob. Netw. Appl. 23(4), 956–966 (2017)spa
dc.relation.referencesIsasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico, ISBN 8420540250. Pearson (2004)spa
dc.relation.referencesMangasarian, O.: Arbitrary-norm separating plane, Tech. rep. 97-07, Computer Science Department, University Wisconsin, Madison (1997)spa
dc.relation.referencesBradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. Informs J. Comput. 11, 217–238 (1999)spa
dc.relation.referencesRahmani, A.M., Liljeberg, P., Preden, J., Jantsch, A.: Fog Computing in the Internet of Things. Springer, New York (2018). ISBN 978-3-319-57638-1, ISBN 978-3-319-57639-8 (eBook)spa
dc.relation.referencesGangurde, H.D.: Feature selection using clustering approach for big data, Int. J. Comput. Appl. (0975–8887) Innovations and Trends in Computer and Communication Engineering (ITCCE), pp. 1–3 (2014)spa
dc.relation.referencesAbualigah, L.M., Khader, A.T., Al-Beta, M.A., Alomari, O.A.: Text feature selection with a robust weight scheme and dynamic dimension reduction to text document clustering. Expert Syst. Appl. 84, 24–36 (2017)spa
dc.relation.referencesSanchez, L., Vásquez, C., Viloria, A., Cmeza-estrada: 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.) DMBD 2018. LNCS, vol. 10943. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93803-5_71spa
dc.relation.referencesSánchez, L., Vásquez, C., Viloria, A., Rodríguez Potes, L.: Greenhouse gases emissions and electric power generation in latin American countries in the period 2006–2013. In: Tan, Y., Shi, Y., Tang, Q. (eds.) DMBD 2018. LNCS, vol. 10943. Springer, Cham (2018). https://doi. org/10.1007/978-3-319-93803-5_73spa
dc.relation.referencesPerez, R., et al.: Fault diagnosis on electrical distribution systems based on fuzzy logic. In: Tan, Y., Shi, Y., Tang, Q. (eds.) ICSI 2018. LNCS, vol. 10942, pp. 174–185. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93818-9_17spa
dc.relation.referencesSuárez, O.M.: Application of the factorial analysis to the investigation of markets. case of study. Sci. Tech. 3(35), 281–286 (2007)spa
dc.relation.referencesBin Mohamad, I., Usman, D.: Standardization and its effects on K-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 6(17), 3299–3303 (2013)spa
dc.relation.referencesCarrasco, Á.: Explicando puntaje Z. Tripod.com (2003). http://aathosc.tripod.com/ PuntajeZ22.htm. Accessed 06 Dec 2017spa
dc.relation.referencesSilva, V., Jesús, A.: Indicators systems for evaluating the efficiency of political awareness of rational use of electricity. In: Advanced Materials Research, vol. 601, pp. 618–625. Trans Tech Publications (2013)spa
dc.relation.referencesPeralta, A., Inga, E., Hincapié, R.: Optimal scalability of FiWi networks based on multistage stochastic programming and policies. J. Opt. Commun. Netw. 9(12), 1172 (2017)spa
dc.relation.referencesRamón, P., Vásquez, C., Viloria, A.: An intelligent strategy for faults location in distribution networks with distributed generation. J. Intell. Fuzzy Systems Preprint, 1–11 (2019)spa
dc.relation.referencesGonen, T.: Electric Power Distribution System Engineering, vol. II. McGraw-Hill, Sacramento (1986)spa
dc.relation.referencesGhia, A., Rosso, A.: Análisis de respuesta de la demanda para mejorar la eficiencia de sistemas eléctricos, 2nd edn. Camara Argentina de la Construccion, Buenos Aires (2009)spa
dc.relation.referencesPérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005)spa
dc.relation.referencesMicrosoft: Microsoft Excel 2016, Microsoft (2016). https://products.office.com/es/excel. Accessed 03 Aug 2017spa
dc.relation.referencesCastañeda, M.B., Cabrera, A., Navarro, Y., Vries, W.: Procesamiento de Datos y Análisis Estadístico usando SPSS, vol. 53, no. 9. Porto Alegre (2010)spa
dc.relation.referencesMathWorks: MathWorks America Latina (2017). https://la.mathworks.com/help/matlab/ index.html. Accessed 25 Aug 2017spa
dc.title.translatedAnálisis y predicción de perfiles de consumo de energía utilizando big dataspa
dc.type.coarhttp://purl.org/coar/resource_type/c_6501spa
dc.type.contentTextspa
dc.type.driverinfo:eu-repo/semantics/articlespa
dc.type.redcolhttp://purl.org/redcol/resource_type/ARTspa
dc.type.versioninfo:eu-repo/semantics/acceptedVersionspa
dc.type.coarversionhttp://purl.org/coar/version/c_ab4af688f83e57aaspa
dc.rights.coarhttp://purl.org/coar/access_right/c_abf2spa


Ficheros en el ítem

Thumbnail
Nombre:
ANALYZING AND PREDICTING POWER ...
Tamaño:
14.42Kb
Formato:
Microsoft Word 2007
Ver/
Thumbnail
Nombre:
license_rdf
Tamaño:
701bytes
Formato:
application/rdf+xml
Ver/

Este ítem aparece en la(s) siguiente(s) colección(ones)

  • Artículos científicos [3154]
    Artículos de investigación publicados por miembros de la comunidad universitaria.

Mostrar el registro sencillo del ítem

CC0 1.0 Universal
Excepto si se señala otra cosa, la licencia del ítem se describe como CC0 1.0 Universal