Abstract
Electricity consumption is an issue that concerns us all. How we use electricity daily affects both the economy and the environment. Many studies analyse the use of electricity in households to predict the energy that will be consumed. Electricity companies are aware of the consumption of households and have estimated the energy that will be needed. However, it would interest to know the different consumer profiles that exist to adjust tariffs to the consumption patterns of users and try to reduce those consumption peaks that cause bills to rise. In this article, an analysis is carried out using clustering techniques to characterise 5,567 households in London from a dataset that includes information on social living standards. The results show that there are many wealthy households with high consumption and poor households with low consumption, as well as households in these same classes with very different consumption.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Alonso, A.M., Nogales, F.J., Ruiz, C.: Hierarchical clustering for smart meter electricity loads based on quantile autocovariances. IEEE Trans. Smart Grid 11(5), 4522–4530 (2020)
Zhang, W., Dong, X., Li, H., Xu, J., Wang, D.: Unsupervised detection of abnormal electricity consumption behavior based on feature engineering. IEEE Access 8, 55483–55500 (2020)
Laurinec, P., Lucká, M.: Interpretable multiple data streams clustering with clipped streams representation for the improvement of electricity consumption forecasting. Data Min. Knowl. Disc. 33(2), 413–445 (2018). https://doi.org/10.1007/s10618-018-0598-2
Sun, M., Konstantelos, I., Strbac, G.: Analysis of diversified residential demand in London using smart meter and demographic data. In: 2016 IEEE Power and Energy Society General Meeting (PESGM), pp. 1–5 (2016)
Pérez-Chacón, R.: Big data analytics for discovering electricity consumption patterns in smart cities. Energies, 11(3), 683 (2018)
Tavenard, R., et al.: Tslearn, a machine learning toolkit for time series data. J. Mach. Learn. Res. 21(118), 1–6 (2020)
UK Power Networks. Smartmeter energy consumption data in London households (2015)
CACI. Understanding consumers and communities (2010)
Ketchen, D.J., Shook, C.L.: The application of cluster analysis in strategic management research: an analysis and critique. Strateg. Manage. J. 17(6), 441–458 (1996)
Acknowledgment
This research has been funded by FEDER/Ministerio de Ciencia, Innovación y Universidades - Agencia Estatal de Investigación/Proyecto TIN2017-88209-C2 and by the Andalusian Regional Government under the projects: BIDASGRI: Big Data technologies for Smart Grids (US-1263341), Adaptive hybrid models to predict solar and wind renewable energy production (P18-RT-2778).
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Luna-Romera, J.M., Carranza-García, M., Gutiérrez-Avilés, D., Riquelme-Santos, J.C. (2022). Study Case of Household Electricity Consumption Patterns in London by Clustering Methodology. In: Sanjurjo González, H., Pastor López, I., García Bringas, P., Quintián, H., Corchado, E. (eds) 16th International Conference on Soft Computing Models in Industrial and Environmental Applications (SOCO 2021). SOCO 2021. Advances in Intelligent Systems and Computing, vol 1401. Springer, Cham. https://doi.org/10.1007/978-3-030-87869-6_67
Download citation
DOI: https://doi.org/10.1007/978-3-030-87869-6_67
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-87868-9
Online ISBN: 978-3-030-87869-6
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)