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Erschienen in:
Decomposition Integral Based Generalizations of OWA Operators Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

Seasonal Clustering of Residential Natural Gas Consumers

verfasst von : Marta P. Fernandes, Joaquim L. Viegas, Susana M. Vieira, João M. C. Sousa

Erschienen in: Information Processing and Management of Uncertainty in Knowledge-Based Systems

Verlag: Springer International Publishing

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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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Vorheriges Kapitel On a Generalized Objective Function for Possibilistic Fuzzy Clustering
Literatur
1.
Azad, S., Wolfs, P., et al.: Identification of typical load profiles using k-means clustering algorithm. In: 2014 Asia-Pacific World Congress on Computer Science and Engineering (APWC on CSE), pp. 1–6. IEEE (2014)
2.
Deepak Sharma, D., Singh, S.: Electrical load profile analysis and peak load assessment using clustering technique. In: PES General Meeting—Conference & Exposition, 2014 IEEE, pp. 1–5. IEEE (2014)
3.
Kim, Y.I., Ko, J.M., Choi, S.H.: Methods for generating TLPs (typical load profiles) for smart grid-based energy programs. In: 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), pp. 1–6. IEEE (2011)
4.
Bidoki, S., Mahmoudi-Kohan, N., Gerami, S.: Comparison of several clustering methods in the case of electrical load curves classification. In: 2011 16th Conference on Electrical Power Distribution Networks (EPDC), pp. 1–7. IEEE (2011)
5.
Sathiracheewin, S., Surapatana, V.: Daily typical load clustering of residential customers. In: 2011 8th International Conference on Electrical Engineering/Electronics, Computer, Telecommunications and Information Technology (ECTI-CON), pp. 797–800. IEEE (2011)
6.
Hossain, M.J., Kabir, A., Rahman, M.M., Kabir, B., Islam, M.R.: Determination of typical load profile of consumers using fuzzy c-means clustering algorithm. International Journal of Soft Computing and Engineering (IJSCE), ISSN-2231-2307(2011)
7.
Lo, K., Zakaria, Z., Sohod, M.: Determination of consumers load profiles based on two-stage fuzzy c-means. In: Proceedings of the 5th WSEAS International Conference on Power Systems and Electromagnetic Compatibility, Corfu, Greece, pp. 212–217 (2005)
8.
Zanotti, G., Gabbi, G., Laboratore, D.: Climate variables and weather derivatives: gas demand, temperature and seasonality effects in the italian case. Temperature and Seasonality Effects in the Italian Case (14/01/2003) (2003)
9.
Dirks, J.A., Gorrissen, W.J., Hathaway, J.H., Skorski, D.C., Scott, M.J., Pulsipher, T.C., Huang, M., Liu, Y., Rice, J.S.: Impacts of climate change on energy consumption and peak demand in buildings: A detailed regional approach. Energy 79, 20–32 (2015)CrossRef
10.
Yang, S.I., Shen, C., et al.: A review of electric load classification in smart grid environment. Renew. Sustain. Energy Rev. 24, 103–110 (2013)CrossRef
11.
Viegas, J.L., Vieira, S.M., Sousa, J.M.C.: Fuzzy clustering and prediction of electricity demand based on household characteristics. In: Proceedings of the16th World Congress of the International Fuzzy Systems Association (IFSA) and the 9th Conference of the European Society for Fuzzy Logic and Technology (EUSFLAT) (2015)
12.
Pkekalska, E., Duin, R.P.: The Dissimilarity Representation for Pattern Recognition: Foundations and Applications, vol. 64. World Scientific, River Edge (2005)
13.
Brabec, M., Konár, O., Malỳ, M., Pelikán, E., Vondráček, J.: A statistical model for natural gas standardized load profiles. J. R. Stat. Soc. Ser. C (Appl. Stat.) 58(1), 123–139 (2009)MathSciNetCrossRef
14.
CER: Smart metering information paper: gas customer behaviour trial findings report, Commission for Energy Regulation (CER). Technical report (2011)
15.
de Oliveira, J.V., Pedrycz, W., et al.: Advances in Fuzzy Clustering and its Applications. Wiley, Chichester (2007)CrossRef
16.
Ward Jr., J.H.: Hierarchical grouping to optimize an objective function. J. Am. Stat. Assoc. 58(301), 236–244 (1963)MathSciNetCrossRef
17.
Metadaten
Titel
Seasonal Clustering of Residential Natural Gas Consumers
verfasst von
Marta P. Fernandes
Joaquim L. Viegas
Susana M. Vieira
João M. C. Sousa
Copyright-Jahr
2016
Buch
Information Processing and Management of Uncertainty in Knowledge-Based Systems

Print ISBN: 978-3-319-40595-7

Electronic ISBN: 978-3-319-40596-4

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-40596-4_60

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