Two-level clustering methodology for smart metering data
Published
Apr 28, 2020
Almetrics
Dimensions
Google Scholar
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
##plugins.themes.bootstrap3.article.details##
Abstract
Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, a necessary functionality is to reveal various preferences, behaviors, and characteristics of individual consumers, considering the energy consumption information from smart meters. In this paper, we introduce a general methodology and a specific two-level clustering approach that can be used to group, considering global and local features, energy consumptions and productions of households. Thus, characteristic load and production profiles can be determined for each consumer and prosumer, respectively. The obtained results will be generally applicable and will be useful in a general business analytics context.
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
How to Cite
Arco García, L., Casas Cardoso, G. M., & Nowé, A. (2020). Two-level clustering methodology for smart metering data. Cuadernos De Administración, 33. https://doi.org/10.11144/Javeriana.cao33.tlcms
Section
Special Knowledge management innovation through fuzzy methodologies