Metodología de agrupación en dos niveles para una medición de datos inteligente
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abr 28, 2020
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Leticia Arco García
http://orcid.org/0000-0002-5154-4441
Gladys María Casas Cardoso
Ann Nowé
http://orcid.org/0000-0001-6346-4564
http://orcid.org/0000-0002-5154-4441
Gladys María Casas Cardoso
Ann Nowé
http://orcid.org/0000-0001-6346-4564
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Resumen
La eficiencia energética y la sostenibilidad son factores importantes a abordar en el contexto de las ciudades inteligentes. En este sentido, una funcionalidad necesaria consiste en revelar varias preferencias, comportamientos y características de los consumidores individuales, considerando la información de consumo de energía de los metro-contadores inteligentes. En este artículo presentamos una metodología general y un enfoque de agrupamiento en dos niveles teniendo en cuenta las características globales y locales del consumo de energía y la producción de los hogares. Por lo tanto, se pueden determinar los perfiles característicos de carga y producción para cada consumidor y prosumidor, respectivamente. Los resultados obtenidos serán de aplicación general y serán útiles en un contexto de análisis empresarial general.
Keywords
Clustering, time series, smart meteringAgrupamiento, series de tiempo, medición inteligenteAgrupação, séries de tempo, medição inteligente
References
Aghabozorgi, S., Saybani, M., & Wah, T. (2012). Incremental clustering of time-series by fuzzy clustering. Journal of Information Science and Engineering, 28, 671-688. https://www.iis.sinica.edu.tw/page/jise/2012/201207_03.pdf
Aghabozorgi, S., Ying Wah, T., Herawan, T., Jalab, H., Shaygan, M., & Jalali, A. (2014). A hybrid algorithm for clustering of time series data based on affinity search technique. Scientific World Journal, 2014, 1301-1314. https://doi.org/10.1155/2014/562194
Ahmad, T., Chen, H., Wang, J., & Guo, Y. (2018). Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renewable & Sustainable Energy Review, 82(August), 2916-2933. https://doi.org/10.1016/j.rser.2017.10.040
Albert, A., & Rajagopal, R. (2013). Smart meter driven segmentation: What your consumption says about you. IEEE Transaction. Power Systems, 28, 4019-4030. https://doi.org/10.1109/TPWRS.2013.2266122
Alzate, C., Espinoza, M., De Moor, B., & Suykens, J. (2009). Identifying customer profiles in power load time series using spectral clustering. Artificial Neural Networks – ICANN, 2009, LNCS 5769, 315-324. https://doi.org/10.1007/978-3-642-04277-5_32
Ardakanian, O., Koochakzadeh, N., Singh, R., Golab, L., & Keshav, S. (2014). Computing electricity consumption profiles from household smart meter data. In EDBT Workshop on Energy Data Management (pp. 140-147). https://pdfs.semanticscholar.org/11b9/5c1d7861e7932919394f65487b551ab3e1cd.pdf
Binh, P., Ha, N., Tuan, T., & Khoa, L. (2010). Determination of representative load curve based on Fuzzy K-Means. 4th International Power Engineering and Optimization Conference (PEOCO), Shah Alam, pp. 281-286. https://doi.org/10.1109/PEOCO.2010.5559257
Brockwell, P., & Davis, R. (2002). Introduction to time series and forecasting, 2 ed. Springer Texts in Statistics. New York: Springer Verlag.
Cao, H., Beckel, C., & Staake, T. (2013). Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns, in 39th Annual Conference of the IEEE Industrial Electronics Society (pp. 4733-4738). IECON. https://doi.org/10.1109/IECON.2013.6699900
Capozzoli, A., Piscitelli, M., Brandi, S., Grassi, D., & Chicco, G. (2018). Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings. Energy, 157, 336-352. https://doi.org/10.1016/j.energy.2018.05.127
Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42(1), 68-80. DOI: 10.1016/j.energy.2011.12.031
Defays, D. (1977). An efficient algorithm for a complete link method. The Computer Journal, 20(4), 364-366. https://doi.org/10.1093/comjnl/20.4.364.
Dent, I., Aickelin, U., & Rodden, T. (2011). Application of a clustering framework to UK domestic electricity data. Ukci, 161-166. https://arxiv.org/abs/1307.1079
Espinoza, M., Joye, C., Belmans, R., & DeMoor, B. (2005). Short-Term load forecasting, profile identification, and customer segmentation: A methodology based on periodic time series. IEEE Transaction. Power Systems, 20(3), 1622-1630. https://doi.org/10.1109/TPWRS.2005.852123
European Commission. (2014). Benchmarking smart metering deployment in the EU-27 with a focus on electricity. Reports. Publications Office of the European Union https://ses.jrc.ec.europa.eu/publications/reports/benchmarking-smart-metering-deployment-eu-27-focus-electricity
Fenza, G., Gallo, M., & Loia, V. (2019). Drift-aware methodology for anomaly detection in smart grid. IEEE Access, 7, 9645-9657. https://doi.org/10.1109/ACCESS.2019.2891315
Figueiredo, V., Rodrigues, F. Vale, Z., & Gouveia, J. (2005). An electric energy consumer characterization framework based on data mining techniques. IEEE Transaction. Power Systems, 20(2), 596-602. https://doi.org/10.1109/TPWRS.2005.846234
Flath, C., Nicolay, D., Conte, T., Van Dinther, C., & Filipova-Neumann, L. (2012). Cluster analysis of smart metering data: An implementation in practice. Business & Information Systems Engineering, 4, 31-39. https://doi.org/10.1007/s12599-011-0201-5
Funde, N., Dhabu, M., Paramasivam, A., & Deshpande, P. (2019). Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data. Sustainable Cities Society, 46(January), 101415. https://doi.org/10.1016/j.scs.2018.12.043
Giordano, V., Gangale, F., Fulli, G., & Sánchez, M. (2011). Smart grids projects in Europe: Lessons learned and current developments. Federal Energy Regulatory Commission. https://ses.jrc.ec.europa.eu/sites/ses/files/documents/smart_grid_projects_in_europe.pdf
Hossain, J., Kabir, A., Rahman, M., Kabir, B., & Islam, R. (2011). Determination of typical load profile of consumers using fuzzy c-means clustering algorithm. Int. J. Soft Comput. Eng., 1(5), 169-173. https://es.scribd.com/document/349605721/Determination-of-Typical-Load-Profile-of-Consumers-Using-Fuzzy-C-Means-Clustering-Algorithm
Hübner, M., & Prüggler, N. (2011). Smart grids initiatives in Europe - Country snapshots and country fact sheets. JRC Reference Reports. Austrian Energy Agency. https://ses.jrc.ec.europa.eu/sites/ses/files/documents/smart_grid_projects_in_europe.pdf
Iglesias, F., & Kastner, W. (2013). Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies, 6, 579-597. https://doi.org/10.3390/en6020579
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern., 43(1), 59-69. https://doi.org/10.1007/BF00337288
Lai, C.-P., Chung, P.-C., & Tseng, V. S. (2010). A novel two-level clustering method for time series data analysis. Expert Systems with Applications, 37(9), 6319-6326. https://doi.org/10.1016/j.eswa.2010.02.089
Lavin, A., & Klabjan, D. (2014). Clustering time ‐ series energy data from smart meters. Energy Efficiency, 8(4), 1-9. https://doi.org/10.1007/s12053-014-9316-0
Lee, T., Haben, S., & Grindrod, P. (2014). Modelling the electricity consumption of small to medium enterprises. In The 18th European Conference on Mathematics for Industry Conference (pp. 1-7). Taormina, Italy: ECMI.
Losa, I., De Nigris, M., & Van, T. (2013). Analysis of the on-going research and demonstration efforts on smart grids in Europe, 22nd International Conference on Electricity Distribution, June, 10-13. https://doi.org/10.1049/cp.2013.0958
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium Mathematical Statistics and Probability, 1, 281-297.
McLoughlin, F., Duffy, A., & Conlon, M. (2012). Analysing domestic electricity smart metering data using self organising maps. In CIRED 2012 Workshop: Integration of Renewables into the Distribution Grid, pp. 319-319. https://doi.org/10.1049/cp.2012.0865
Mutanen, A., Ruska, M., Repo, S., & Järventausta, P. (2011). Customer classification and load profiling method for distribution systems. IEEE Trans. Power Deliv., 26, 1755-1763. https://doi.org/10.1109/TPWRD.2011.2142198
Nanopoulos, A., Alcock, R., & Manolopoulos, Y. (2001). Feature-based classification of time-series data. International Journal of Computer Research, 10, 49-61. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.73.9555
Oates, T., Firoiu, L., & Cohen, P. (1999). Clustering time series with hidden Markov Models and dynamic time warping. In Proceedings of the IJCAI-99 Workshop on Neural, symbolic and reinforcement learning methods for sequence learning (pp. 17-21).
Räsänen, T., & Kolehmainen, M. (2009). Feature-based clustering for electricity use time series data. In 9th International Conference, ICANNGA 2009, LNCS 5495, 401-412. https://doi.org/10.1007/978-3-642-04921-7_41
Räsänen, T., Voukantsis, D., Niska, H., Karatzas, K., & Kolehmainen, M. (2010). Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energy, 87, 3538-3545. https://doi.org/10.1016/j.apenergy.2010.05.015
Renner, S., & Heinemann, C. (2011). European Smart Metering Landscape Report, 2(February), 168 p. https://www.sintef.no/globalassets/project/smartregions/d2.1_european-smart-metering-landscape-report_final.pdf
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8), 888-905. https://doi.org/10.1109/34.868688
Wang, X., Smith, K., & Hyndman, R. (2006). Characteristic-based clustering for time series data. Data Min. Knowl. Discov., 13, 335-364. https://doi.org/10.1007/s10618-005-0039-x
Wang, X., Smith, K., Hyndman, R., & Alahakoon, D. (2004). A scalable method for time series clustering. Research of Monash University. Victoria, Australia: Monash University. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.207
Warren Liao, T. (2007). A clustering procedure for exploratory mining of vector time series. Pattern Recognit., 40, 2550-2562. https://doi.org/10.1016/j.patcog.2007.01.005
Zhang, X., Liu, J., Du, Y., & Lv, T. (2011). A novel clustering method on time series data. Expert Systems Applications, 38, 11891-11900. https://doi.org/10.1016/j.eswa.2011.03.081
Aghabozorgi, S., Ying Wah, T., Herawan, T., Jalab, H., Shaygan, M., & Jalali, A. (2014). A hybrid algorithm for clustering of time series data based on affinity search technique. Scientific World Journal, 2014, 1301-1314. https://doi.org/10.1155/2014/562194
Ahmad, T., Chen, H., Wang, J., & Guo, Y. (2018). Review of various modeling techniques for the detection of electricity theft in smart grid environment. Renewable & Sustainable Energy Review, 82(August), 2916-2933. https://doi.org/10.1016/j.rser.2017.10.040
Albert, A., & Rajagopal, R. (2013). Smart meter driven segmentation: What your consumption says about you. IEEE Transaction. Power Systems, 28, 4019-4030. https://doi.org/10.1109/TPWRS.2013.2266122
Alzate, C., Espinoza, M., De Moor, B., & Suykens, J. (2009). Identifying customer profiles in power load time series using spectral clustering. Artificial Neural Networks – ICANN, 2009, LNCS 5769, 315-324. https://doi.org/10.1007/978-3-642-04277-5_32
Ardakanian, O., Koochakzadeh, N., Singh, R., Golab, L., & Keshav, S. (2014). Computing electricity consumption profiles from household smart meter data. In EDBT Workshop on Energy Data Management (pp. 140-147). https://pdfs.semanticscholar.org/11b9/5c1d7861e7932919394f65487b551ab3e1cd.pdf
Binh, P., Ha, N., Tuan, T., & Khoa, L. (2010). Determination of representative load curve based on Fuzzy K-Means. 4th International Power Engineering and Optimization Conference (PEOCO), Shah Alam, pp. 281-286. https://doi.org/10.1109/PEOCO.2010.5559257
Brockwell, P., & Davis, R. (2002). Introduction to time series and forecasting, 2 ed. Springer Texts in Statistics. New York: Springer Verlag.
Cao, H., Beckel, C., & Staake, T. (2013). Are domestic load profiles stable over time? An attempt to identify target households for demand side management campaigns, in 39th Annual Conference of the IEEE Industrial Electronics Society (pp. 4733-4738). IECON. https://doi.org/10.1109/IECON.2013.6699900
Capozzoli, A., Piscitelli, M., Brandi, S., Grassi, D., & Chicco, G. (2018). Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings. Energy, 157, 336-352. https://doi.org/10.1016/j.energy.2018.05.127
Chicco, G. (2012). Overview and performance assessment of the clustering methods for electrical load pattern grouping. Energy, 42(1), 68-80. DOI: 10.1016/j.energy.2011.12.031
Defays, D. (1977). An efficient algorithm for a complete link method. The Computer Journal, 20(4), 364-366. https://doi.org/10.1093/comjnl/20.4.364.
Dent, I., Aickelin, U., & Rodden, T. (2011). Application of a clustering framework to UK domestic electricity data. Ukci, 161-166. https://arxiv.org/abs/1307.1079
Espinoza, M., Joye, C., Belmans, R., & DeMoor, B. (2005). Short-Term load forecasting, profile identification, and customer segmentation: A methodology based on periodic time series. IEEE Transaction. Power Systems, 20(3), 1622-1630. https://doi.org/10.1109/TPWRS.2005.852123
European Commission. (2014). Benchmarking smart metering deployment in the EU-27 with a focus on electricity. Reports. Publications Office of the European Union https://ses.jrc.ec.europa.eu/publications/reports/benchmarking-smart-metering-deployment-eu-27-focus-electricity
Fenza, G., Gallo, M., & Loia, V. (2019). Drift-aware methodology for anomaly detection in smart grid. IEEE Access, 7, 9645-9657. https://doi.org/10.1109/ACCESS.2019.2891315
Figueiredo, V., Rodrigues, F. Vale, Z., & Gouveia, J. (2005). An electric energy consumer characterization framework based on data mining techniques. IEEE Transaction. Power Systems, 20(2), 596-602. https://doi.org/10.1109/TPWRS.2005.846234
Flath, C., Nicolay, D., Conte, T., Van Dinther, C., & Filipova-Neumann, L. (2012). Cluster analysis of smart metering data: An implementation in practice. Business & Information Systems Engineering, 4, 31-39. https://doi.org/10.1007/s12599-011-0201-5
Funde, N., Dhabu, M., Paramasivam, A., & Deshpande, P. (2019). Motif-based association rule mining and clustering technique for determining energy usage patterns for smart meter data. Sustainable Cities Society, 46(January), 101415. https://doi.org/10.1016/j.scs.2018.12.043
Giordano, V., Gangale, F., Fulli, G., & Sánchez, M. (2011). Smart grids projects in Europe: Lessons learned and current developments. Federal Energy Regulatory Commission. https://ses.jrc.ec.europa.eu/sites/ses/files/documents/smart_grid_projects_in_europe.pdf
Hossain, J., Kabir, A., Rahman, M., Kabir, B., & Islam, R. (2011). Determination of typical load profile of consumers using fuzzy c-means clustering algorithm. Int. J. Soft Comput. Eng., 1(5), 169-173. https://es.scribd.com/document/349605721/Determination-of-Typical-Load-Profile-of-Consumers-Using-Fuzzy-C-Means-Clustering-Algorithm
Hübner, M., & Prüggler, N. (2011). Smart grids initiatives in Europe - Country snapshots and country fact sheets. JRC Reference Reports. Austrian Energy Agency. https://ses.jrc.ec.europa.eu/sites/ses/files/documents/smart_grid_projects_in_europe.pdf
Iglesias, F., & Kastner, W. (2013). Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies, 6, 579-597. https://doi.org/10.3390/en6020579
Kohonen, T. (1982). Self-organized formation of topologically correct feature maps. Biol. Cybern., 43(1), 59-69. https://doi.org/10.1007/BF00337288
Lai, C.-P., Chung, P.-C., & Tseng, V. S. (2010). A novel two-level clustering method for time series data analysis. Expert Systems with Applications, 37(9), 6319-6326. https://doi.org/10.1016/j.eswa.2010.02.089
Lavin, A., & Klabjan, D. (2014). Clustering time ‐ series energy data from smart meters. Energy Efficiency, 8(4), 1-9. https://doi.org/10.1007/s12053-014-9316-0
Lee, T., Haben, S., & Grindrod, P. (2014). Modelling the electricity consumption of small to medium enterprises. In The 18th European Conference on Mathematics for Industry Conference (pp. 1-7). Taormina, Italy: ECMI.
Losa, I., De Nigris, M., & Van, T. (2013). Analysis of the on-going research and demonstration efforts on smart grids in Europe, 22nd International Conference on Electricity Distribution, June, 10-13. https://doi.org/10.1049/cp.2013.0958
MacQueen, J. (1967). Some methods for classification and analysis of multivariate observations. In Proceedings of the 5th Berkeley Symposium Mathematical Statistics and Probability, 1, 281-297.
McLoughlin, F., Duffy, A., & Conlon, M. (2012). Analysing domestic electricity smart metering data using self organising maps. In CIRED 2012 Workshop: Integration of Renewables into the Distribution Grid, pp. 319-319. https://doi.org/10.1049/cp.2012.0865
Mutanen, A., Ruska, M., Repo, S., & Järventausta, P. (2011). Customer classification and load profiling method for distribution systems. IEEE Trans. Power Deliv., 26, 1755-1763. https://doi.org/10.1109/TPWRD.2011.2142198
Nanopoulos, A., Alcock, R., & Manolopoulos, Y. (2001). Feature-based classification of time-series data. International Journal of Computer Research, 10, 49-61. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.73.9555
Oates, T., Firoiu, L., & Cohen, P. (1999). Clustering time series with hidden Markov Models and dynamic time warping. In Proceedings of the IJCAI-99 Workshop on Neural, symbolic and reinforcement learning methods for sequence learning (pp. 17-21).
Räsänen, T., & Kolehmainen, M. (2009). Feature-based clustering for electricity use time series data. In 9th International Conference, ICANNGA 2009, LNCS 5495, 401-412. https://doi.org/10.1007/978-3-642-04921-7_41
Räsänen, T., Voukantsis, D., Niska, H., Karatzas, K., & Kolehmainen, M. (2010). Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energy, 87, 3538-3545. https://doi.org/10.1016/j.apenergy.2010.05.015
Renner, S., & Heinemann, C. (2011). European Smart Metering Landscape Report, 2(February), 168 p. https://www.sintef.no/globalassets/project/smartregions/d2.1_european-smart-metering-landscape-report_final.pdf
Shi, J., & Malik, J. (2000). Normalized cuts and image segmentation. IEEE Trans. Pattern Anal. Mach. Intell., 22(8), 888-905. https://doi.org/10.1109/34.868688
Wang, X., Smith, K., & Hyndman, R. (2006). Characteristic-based clustering for time series data. Data Min. Knowl. Discov., 13, 335-364. https://doi.org/10.1007/s10618-005-0039-x
Wang, X., Smith, K., Hyndman, R., & Alahakoon, D. (2004). A scalable method for time series clustering. Research of Monash University. Victoria, Australia: Monash University. http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.155.207
Warren Liao, T. (2007). A clustering procedure for exploratory mining of vector time series. Pattern Recognit., 40, 2550-2562. https://doi.org/10.1016/j.patcog.2007.01.005
Zhang, X., Liu, J., Du, Y., & Lv, T. (2011). A novel clustering method on time series data. Expert Systems Applications, 38, 11891-11900. https://doi.org/10.1016/j.eswa.2011.03.081
Cómo citar
Arco García, L., Casas Cardoso, G. M., & Nowé, A. (2020). Metodología de agrupación en dos niveles para una medición de datos inteligente. Cuadernos De Administración, 33. https://doi.org/10.11144/Javeriana.cao33.tlcms
Sección
Especial Innovación en la gestión del conocimiento a través de metodologías