Abstract
The household characteristics in an electric grid include the socio-economic status of households, the dwelling properties, the information on the appliance stock, and so forth. These characteristics are significantly beneficial to electric retailers, because they can be utilized to provide personalized services, improve the demand response, and make better energy efficiency programs. However, these privacy-sensitive characteristics (e.g., employment, income, age of residents) require time-consuming surveys. Also, it is difficult to gather such residential household information in a large scale. In recent years, the increasing availability of electricity consumption data makes it possible to infer household characteristics from residential electricity consumption data. A number of supervised learning methods have been proposed. Among these solutions, features are extracted from the electricity consumption patterns, and the selected features are used to train a classifier or regressor. However, the existed methods depend on a single contributing model, which can be possibly undertrained. To achieve the optimal performance of classifiers for characteristics identification, we propose an ensemble framework based on bagging algorithms. With the proposed ensemble framework, the performance of characteristic identification has been improved.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Commission for Energy Regulation (CER): CER smart metering project - electricity customer behaviour trial, 2009–2010 [dataset] (2012), 1st edn. Irish Social Science Data Archive. SN: 0012–00 https://www.ucd.ie/issda/data/commissionforenergyregulationcer/
Bauer, E., Kohavi, R.: An empirical comparison of voting classification algorithms: bagging, boosting, and variants. Mach. Learn. 36(1), 105–139 (1999)
Beckel, C., Sadamori, L., Santini, S.: Automatic socio-economic classification of households using electricity consumption data. In: Proceedings of the Fourth International Conference on Future Energy Systems, pp. 75–86 (2013)
Beckel, C., Sadamori, L., Staake, T., Santini, S.: Revealing household characteristics from smart meter data. Energy 78, 397–410 (2014)
Breiman, L.: Bagging predictors. Machine learning 24(2), 123–140 (1996)
Dang, Q., Wu, D., Boulet, B.: An advanced framework for electric vehicles interaction with distribution grids based on q-learning. In: 2019 IEEE Energy Conversion Congress and Exposition (ECCE), pp. 3491–3495. IEEE (2019)
Dang, Q., Wu, D., Boulet, B.: A q-learning based charging scheduling scheme for electric vehicles. In: 2019 IEEE Transportation Electrification Conference and Expo (ITEC). pp. 1–5. IEEE (2019)
Dang, Q., Wu, D., Boulet, B.: EV charging management with ANN-based electricity price forecasting. In: 2020 IEEE Transportation Electrification Conference & Expo (ITEC), pp. 626–630. IEEE (2020)
Huang, X., Wu, D., Boulet, B.: Ensemble learning for charging load forecasting of electric vehicle charging stations. In: 2020 IEEE Electric Power and Energy Conference (EPEC), pp. 1–5. IEEE (2020)
Jiang, T., Li, J., Zheng, Y., Sun, C.: Improved bagging algorithm for pattern recognition in uhf signals of partial discharges. Energies 4(7), 1087–1101 (2011)
Kuncheva, L.I.: Combining pattern classifiers: methods and algorithms. John Wiley & Sons, New York (2014)
Lin, W., Wu, D.: Residential electric load forecasting via attentive transfer of graph neural networks. In: IJCAI, pp. 2716–2722. ijcai.org (2021)
Lin, W., Wu, D., Boulet, B.: Spatial-temporal residential short-term load forecasting via graph neural networks. IEEE Trans. Smart Grid 12(6), 5373–5384 (2021)
Opitz, J., Burst, S.: Macro f1 and macro f1. arXiv preprint arXiv:1911.03347 (2019)
Sagi, O., Rokach, L.: Ensemble learning: a survey. Wiley Interdiscip. Rev. Data Mining Knowl. Discov 8(4), e1249 (2018)
Wang, Y., Bennani, I.L., Liu, X., Sun, M., Zhou, Y.: Electricity consumer characteristics identification: a federated learning approach. IEEE Trans. Smart Grid 12, 3637–3647 (2021)
Wang, Y., Chen, Q., Gan, D., Yang, J., Kirschen, D.S., Kang, C.: Deep learning-based socio-demographic information identification from smart meter data. IEEE Trans. Smart Grid 10(3), 2593–2602 (2018)
Wang, Y., Chen, Q., Kang, C., Xia, Q., Luo, M.: Sparse and redundant representation-based smart meter data compression and pattern extraction. IEEE Trans. Power Syst. 32(3), 2142–2151 (2016)
Wu, D.: Machine Learning Algorithms and Applications for Sustainable Smart Grid. McGill University, Montreal (2018)
Wu, D., Wang, B., Precup, D., Boulet, B.: Boosting based multiple kernel learning and transfer regression for electricity load forecasting. In: Altun, Y. et al. (eds.) Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2017. LNCS, vol. 10536, pp. 39–51. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-71273-4_4
Wu, D., Wang, B., Precup, D., Boulet, B.: Multiple kernel learning-based transfer regression for electric load forecasting. IEEE Trans. Smart Grid 11(2), 1183–1192 (2019)
Wu, D., Zeng, H., Boulet, B.: Neighborhood level network aware electric vehicle charging management with mixed control strategy. In: 2014 IEEE International Electric Vehicle Conference (IEVC), pp. 1–7. IEEE (2014)
Wu, D., Zeng, H., Lu, C., Boulet, B.: Two-stage energy management for office buildings with workplace EV charging and renewable energy. IEEE Trans. Transp. Electr. 3(1), 225–237 (2017)
Yan, S., et al.: Time-frequency feature combination based household characteristic identification approach using smart meter data. IEEE Trans. Ind. Appl. 56(3), 2251–2262 (2020)
Zhang, C., Ma, Y.: Ensemble Machine Learning: Methods and Applications. Springer, Cham (2012)
Zhong, S., Tam, K.S.: Hierarchical classification of load profiles based on their characteristic attributes in frequency domain. IEEE Trans. Power Syst. 30(5), 2434–2441 (2014)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 IFIP International Federation for Information Processing
About this paper
Cite this paper
Lin, W., Wu, D. (2022). Characterization of Residential Electricity Customers via Deep Ensemble Learning. In: Mercier-Laurent, E., Kayakutlu, G. (eds) Artificial Intelligence for Knowledge Management, Energy, and Sustainability. AI4KMES 2021. IFIP Advances in Information and Communication Technology, vol 637. Springer, Cham. https://doi.org/10.1007/978-3-030-96592-1_6
Download citation
DOI: https://doi.org/10.1007/978-3-030-96592-1_6
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-96591-4
Online ISBN: 978-3-030-96592-1
eBook Packages: Computer ScienceComputer Science (R0)