Analyzing and predicting power consumption profiles using big data
Análisis y predicción de perfiles de consumo de energía utilizando big data
Date
2019
2019
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Abstract
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
Referencias:
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)
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Carrasco, Á.: Explicando puntaje Z. Tripod.com (2003). http://aathosc.tripod.com/ PuntajeZ22.htm. Accessed 06 Dec 2017
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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
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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