Abstract
This article presents the application of computational intelligence techniques for the characterization of electricity consumption in households. A specific variant of the residential energy disaggregation problem is solved, which proposes identifying the state changes in a electrical network, using a time series of aggregate household power consumption data. This article introduces and compares three classifiers to solve the problem: a Naive Bayes classifier, a K Nearest Neighbors algorithm, and a Long Short Term Memory neural network. The implemented classifiers are evaluated using the UK-DALE data repository. Experimental results show that the Long Short Term Memory network is the most accurate to deal with the characterization problem, achieving a successful rate of state changes up to 75% and values of F1-score close to 1.0 on certain appliances.
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References
Batra, N., et al.: NILMTK: an open source toolkit for non-intrusive load monitoring. In: 5th International Conference on Future Energy Systems, pp. 265–276 (2014)
Bonfigli, R., Squartini, S., Fagiani, M., Piazza, F.: Unsupervised algorithms for non-intrusive load monitoring: an up-to-date overview. In: 15th International Conference on Environment and Electrical Engineering (2015)
Chavat, J., Graneri, J., Nesmachnow, S.: Household energy disaggregation based on pattern consumption similarities. In: Nesmachnow, S., Hernández Callejo, L. (eds.) ICSC-CITIES 2019. CCIS, vol. 1152, pp. 54–69. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-38889-8_5
Chavat, J., Nesmachnow, S., Graneri, J.: Non-intrusive energy disaggregation by detecting similarities in consumption patterns. Revista Facultad de Ingeniería Universidad de Antioquia (2020)
Hart, G.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)
Kelly, J., Knottenbelt, W.: The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Sci. Data 2(150007), 1–14 (2015)
Kolter, J., Johnson, M.: Redd: a public data set for energy disaggregation research. In: Workshop on Data Mining Applications in Sustainability, pp. 59–62 (2011)
Luján, E., Otero, A., Valenzuela, S., Mocskos, E.E., Steffenel, L.A., Nesmachnow, S.: An integrated platform for smart energy management: the CC-SEM project. Revista Facultad de Ingeniería Universidad de Antioquia (2019)
Nesmachnow, S., Iturriaga, S.: Cluster-UY: collaborative scientific high performance computing in Uruguay. In: Torres, M., Klapp, J. (eds.) ISUM 2019. CCIS, vol. 1151, pp. 188–202. Springer, Cham (2019). https://doi.org/10.1007/978-3-030-38043-4_16
Sokolova, M., Japkowicz, N., Szpakowicz, S.: Beyond accuracy, F-score and ROC: a family of discriminant measures for performance evaluation. In: Sattar, A., Kang, B. (eds.) AI 2006. LNCS (LNAI), vol. 4304, pp. 1015–1021. Springer, Heidelberg (2006). https://doi.org/10.1007/11941439_114
Turner, W., Doty, S.: Energy management handbook. The Fairmont Press, Lilburn (2007)
U.S. Energy Information Administration (EIA): Energy use in homes, June 2020. https://www.eia.gov/
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Esteban, M., Fiori, I., Mujica, M., Nesmachnow, S. (2021). Computational Intelligence for Characterization and Disaggregation of Residential Electricity Consumption. In: Nesmachnow, S., Hernández Callejo, L. (eds) Smart Cities. ICSC-CITIES 2020. Communications in Computer and Information Science, vol 1359. Springer, Cham. https://doi.org/10.1007/978-3-030-69136-3_5
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