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.
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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).
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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
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DOI: https://doi.org/10.1007/978-3-030-87869-6_67
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