Abstract
This paper proposes a methodology to define the seasonal load profiles of residential gas consumers using smart metering data. A detailed clustering analysis is performed using fuzzy c-means, k-means and hierarchical clustering algorithms with multiple clustering validity indices. The analysis is based on a sample of more than one thousand households over one year. The results provide evidence that crisp algorithms present the best clustering results overall. However, the fuzzy algorithm proves to be suited when the others generate clusters which are not representative of population groups. Compact and well defined seasonal clusters of gas consumers are obtained, where the representative profiles reflect the consumption patterns that vary according to the season of the year. The knowledge obtained with this methodology can assist decision makers in the energy utilities in developing demand side management programs, consumer engagement strategies, marketing, as well as in designing innovative tariff systems.
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Acknowledgements
This work is supported by the Portuguese Government under the program SusCity, through FCT/MEC (under the Unit IDMEC - Pole IST, Research Group IDMEC/CSI), project MITP-TB/CS/0026/2013. The work of J. L. Viegas was supported by the PhD in Industry Scholarship SFRH/BDE/95414/2013 from FCT and Novabase. S. Vieira acknowledges support by Program Investigador FCT (IF/00833/ 2014) from FCT, co-funded by the European Social Fund (ESF) through the Operational Program Human Potential (POPH).
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Fernandes, M.P., Viegas, J.L., Vieira, S.M., Sousa, J.M.C. (2016). Seasonal Clustering of Residential Natural Gas Consumers. In: Carvalho, J., Lesot, MJ., Kaymak, U., Vieira, S., Bouchon-Meunier, B., Yager, R. (eds) Information Processing and Management of Uncertainty in Knowledge-Based Systems. IPMU 2016. Communications in Computer and Information Science, vol 610. Springer, Cham. https://doi.org/10.1007/978-3-319-40596-4_60
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DOI: https://doi.org/10.1007/978-3-319-40596-4_60
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