Analyzing and predicting power consumption profiles using big data

The 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 be...

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Autores:
amelec, viloria
Prieto Pulido, Ronald Antonio
García Guiliany, Jesús
Martínez Ventura, Jairo
Hernández Palma, Hugo
Jinete Torres, José
REDONDO BILBAO, OSMAN ENRIQUE
Pineda Lezam, Omar Bonerge
Tipo de recurso:
Article of journal
Fecha de publicación:
2019
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/5828
Acceso en línea:
https://hdl.handle.net/11323/5828
https://repositorio.cuc.edu.co/
Palabra clave:
Prediction
Power consumption
Big Data
ARIMA
Predicción
Consumo de energía
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_f7140a16971e1d8eaee19707e1a9ae52
oai_identifier_str oai:repositorio.cuc.edu.co:11323/5828
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Analyzing and predicting power consumption profiles using big data
dc.title.translated.spa.fl_str_mv Análisis y predicción de perfiles de consumo de energía utilizando big data
title Analyzing and predicting power consumption profiles using big data
spellingShingle Analyzing and predicting power consumption profiles using big data
Prediction
Power consumption
Big Data
ARIMA
Predicción
Consumo de energía
title_short Analyzing and predicting power consumption profiles using big data
title_full Analyzing and predicting power consumption profiles using big data
title_fullStr Analyzing and predicting power consumption profiles using big data
title_full_unstemmed Analyzing and predicting power consumption profiles using big data
title_sort Analyzing and predicting power consumption profiles using big data
dc.creator.fl_str_mv amelec, viloria
Prieto Pulido, Ronald Antonio
García Guiliany, Jesús
Martínez Ventura, Jairo
Hernández Palma, Hugo
Jinete Torres, José
REDONDO BILBAO, OSMAN ENRIQUE
Pineda Lezam, Omar Bonerge
dc.contributor.author.spa.fl_str_mv amelec, viloria
Prieto Pulido, Ronald Antonio
García Guiliany, Jesús
Martínez Ventura, Jairo
Hernández Palma, Hugo
Jinete Torres, José
REDONDO BILBAO, OSMAN ENRIQUE
Pineda Lezam, Omar Bonerge
dc.subject.spa.fl_str_mv Prediction
Power consumption
Big Data
ARIMA
Predicción
Consumo de energía
topic Prediction
Power consumption
Big Data
ARIMA
Predicción
Consumo de energía
description The 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 expenses
publishDate 2019
dc.date.issued.none.fl_str_mv 2019
dc.date.accessioned.none.fl_str_mv 2020-01-15T19:30:28Z
dc.date.available.none.fl_str_mv 2020-01-15T19:30:28Z
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identifier_str_mv 18650929
Corporación Universidad de la Costa
REDICUC - Repositorio CUC
url https://hdl.handle.net/11323/5828
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dc.language.iso.none.fl_str_mv eng
language eng
dc.relation.ispartof.spa.fl_str_mv https://doi.org/10.1007/978-981-15-1304-6_31
dc.relation.references.spa.fl_str_mv Bradley, 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)
Hu, 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)
Xue, 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)
Wang, 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)
Rong, L., Guosheng, F., Weidai, D.: Statistical Analysis and Application of SAS. China Machine Press, Beijing (2011)
. 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)
Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico, ISBN 8420540250. Pearson (2004)
Mangasarian, O.: Arbitrary-norm separating plane, Tech. rep. 97-07, Computer Science Department, University Wisconsin, Madison (1997)
Bradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. Informs J. Comput. 11, 217–238 (1999)
Rahmani, 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)
Gangurde, 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)
Abualigah, 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)
Sanchez, 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_71
Sánchez, 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_73
Perez, 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_17
Suárez, O.M.: Application of the factorial analysis to the investigation of markets. case of study. Sci. Tech. 3(35), 281–286 (2007)
Bin Mohamad, I., Usman, D.: Standardization and its effects on K-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 6(17), 3299–3303 (2013)
Carrasco, Á.: Explicando puntaje Z. Tripod.com (2003). http://aathosc.tripod.com/ PuntajeZ22.htm. Accessed 06 Dec 2017
Silva, 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)
Peralta, 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)
Ramón, 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)
Gonen, T.: Electric Power Distribution System Engineering, vol. II. McGraw-Hill, Sacramento (1986)
Ghia, 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)
Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005)
Microsoft: Microsoft Excel 2016, Microsoft (2016). https://products.office.com/es/excel. Accessed 03 Aug 2017
Castañeda, M.B., Cabrera, A., Navarro, Y., Vries, W.: Procesamiento de Datos y Análisis Estadístico usando SPSS, vol. 53, no. 9. Porto Alegre (2010)
MathWorks: MathWorks America Latina (2017). https://la.mathworks.com/help/matlab/ index.html. Accessed 25 Aug 2017
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spelling amelec, viloriaPrieto Pulido, Ronald AntonioGarcía Guiliany, JesúsMartínez Ventura, JairoHernández Palma, HugoJinete Torres, JoséREDONDO BILBAO, OSMAN ENRIQUEPineda Lezam, Omar Bonerge2020-01-15T19:30:28Z2020-01-15T19:30:28Z201918650929https://hdl.handle.net/11323/5828Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/The 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 expensesLa 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.Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005)Amelec, Viloria-will be generated-orcid-0000-0003-2673-6350-600Prieto Pulido, Ronald Antonio-will be generated-orcid-0000-0003-3901-4250-600García Guiliany, JesúsMartínez Ventura, JairoHernández Palma, HugoJinete Torres, JoséRedondo Bilbao, Osman Enrique-will be generated-orcid-0000-0002-5477-0655-600Pineda Lezam, Omar BonergeengCommunications in Computer and Information Sciencehttps://doi.org/10.1007/978-981-15-1304-6_31Bradley, 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)Hu, 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)Xue, 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)Wang, 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)Rong, L., Guosheng, F., Weidai, D.: Statistical Analysis and Application of SAS. China Machine Press, Beijing (2011). 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)Isasi, P., Galván, I.: Redes de Neuronas Artificiales. Un enfoque Práctico, ISBN 8420540250. Pearson (2004)Mangasarian, O.: Arbitrary-norm separating plane, Tech. rep. 97-07, Computer Science Department, University Wisconsin, Madison (1997)Bradley, P., Fayyad, U., Mangasarian, O.: Mathematical programming for data mining: formulations and challenges. Informs J. Comput. 11, 217–238 (1999)Rahmani, 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)Gangurde, 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)Abualigah, 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)Sanchez, 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_71Sánchez, 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_73Perez, 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_17Suárez, O.M.: Application of the factorial analysis to the investigation of markets. case of study. Sci. Tech. 3(35), 281–286 (2007)Bin Mohamad, I., Usman, D.: Standardization and its effects on K-means clustering algorithm. Res. J. Appl. Sci. Eng. Technol. 6(17), 3299–3303 (2013)Carrasco, Á.: Explicando puntaje Z. Tripod.com (2003). http://aathosc.tripod.com/ PuntajeZ22.htm. Accessed 06 Dec 2017Silva, 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)Peralta, 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)Ramón, 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)Gonen, T.: Electric Power Distribution System Engineering, vol. II. McGraw-Hill, Sacramento (1986)Ghia, 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)Pérez Arriaga, J.I., Sánchez de Tembleque, L.J., Pardo, M.: La gestión de la demanda de electricidad, vol. I, no. I (2005)Microsoft: Microsoft Excel 2016, Microsoft (2016). https://products.office.com/es/excel. Accessed 03 Aug 2017Castañeda, M.B., Cabrera, A., Navarro, Y., Vries, W.: Procesamiento de Datos y Análisis Estadístico usando SPSS, vol. 53, no. 9. Porto Alegre (2010)MathWorks: MathWorks America Latina (2017). https://la.mathworks.com/help/matlab/ index.html. 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