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2018 | Supplement | Buchkapitel

Generating Load Profiles Using Smart Metering Time Series

verfasst von : Christian Bock

Erschienen in: Advances in Fuzzy Logic and Technology 2017

Verlag: Springer International Publishing

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Abstract

In this work we present a practice-oriented approach for generating load profiles as a means to forecast energy demand by using smart metering time series. The general idea is to apply fuzzy clustering on historic consumption time series. The segmentation yielded helps electricity companies to identify customers with similar consumption behavior. This knowledge can be used to plan available energy capacities in advance. What makes this approach special is that this approach segments consumption time series by time in addition to identifying customer groups. This is done not only to accommodate for customers potentially behaving completely different on working days than on local holidays for example, but also to build the resulting load profiles in a way the electricity companies can adapt with minimal adjustments. We also evaluate our approach using two real world smart metering datasets and discuss potential improvements.

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Literatur
2.
Zurück zum Zitat Andersen, F., Larsen, H., Boomsma, T.: Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energ. Convers. Manag. 68, 244–252 (2013)CrossRef Andersen, F., Larsen, H., Boomsma, T.: Long-term forecasting of hourly electricity load: identification of consumption profiles and segmentation of customers. Energ. Convers. Manag. 68, 244–252 (2013)CrossRef
3.
Zurück zum Zitat Beckel, C., Sadamori, L., et al.: Revealing household characteristics from smart meter data. Energy 78, 397–410 (2014)CrossRef Beckel, C., Sadamori, L., et al.: Revealing household characteristics from smart meter data. Energy 78, 397–410 (2014)CrossRef
4.
Zurück zum Zitat Benítez, I., Quijano, A., et al.: Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers. Int. J. Electr. Power Energ. Syst. 55, 437–448 (2014)CrossRef Benítez, I., Quijano, A., et al.: Dynamic clustering segmentation applied to load profiles of energy consumption from Spanish customers. Int. J. Electr. Power Energ. Syst. 55, 437–448 (2014)CrossRef
5.
Zurück zum Zitat Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, New York (1981)CrossRefMATH Bezdek, J.C.: Pattern Recognition with Fuzzy Objective Function Algorithms. Springer, New York (1981)CrossRefMATH
6.
Zurück zum Zitat Bock, C.: Clustering-Ansatz zur Erstellung von Lastprofilen zur Vorhersage des Stromverbrauchs. In: Proceedings of the 28th GI-Workshop Grundlagen von Datenbanken (GvDB 2016), pp. 21–26 (2016) Bock, C.: Clustering-Ansatz zur Erstellung von Lastprofilen zur Vorhersage des Stromverbrauchs. In: Proceedings of the 28th GI-Workshop Grundlagen von Datenbanken (GvDB 2016), pp. 21–26 (2016)
7.
Zurück zum Zitat Bouguessa, M., Wang, S., Sun, H.: An objective approach to cluster validation. Pattern Recogn. Lett. 27(13), 1419–1430 (2006)CrossRef Bouguessa, M., Wang, S., Sun, H.: An objective approach to cluster validation. Pattern Recogn. Lett. 27(13), 1419–1430 (2006)CrossRef
9.
Zurück zum Zitat Chicco, G., Ilie, I.S.: Support vector clustering of electrical load pattern data. IEEE Trans. Power Syst. 24(3), 1619–1628 (2009)CrossRef Chicco, G., Ilie, I.S.: Support vector clustering of electrical load pattern data. IEEE Trans. Power Syst. 24(3), 1619–1628 (2009)CrossRef
10.
Zurück zum Zitat Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids (2015). CoRR, abs/1504.02424 Diamantoulakis, P.D., Kapinas, V.M., Karagiannidis, G.K.: Big data analytics for dynamic energy management in smart grids (2015). CoRR, abs/1504.02424
11.
Zurück zum Zitat Figueiredo, V., Rodrigues, F., et al.: An electric energy consumer characterization framework based on data mining techniques. IEEE Trans. Power Syst. 20(2), 596–602 (2005)CrossRef Figueiredo, V., Rodrigues, F., et al.: An electric energy consumer characterization framework based on data mining techniques. IEEE Trans. Power Syst. 20(2), 596–602 (2005)CrossRef
12.
Zurück zum Zitat Fusco, F., Wurst, M., Yoon, J.: Mining residential household information from low-resolution smart meter data. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3545–3548, November 2012 Fusco, F., Wurst, M., Yoon, J.: Mining residential household information from low-resolution smart meter data. In: 2012 21st International Conference on Pattern Recognition (ICPR), pp. 3545–3548, November 2012
13.
Zurück zum Zitat Gath, I., Geva, A.: Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11, 773–780 (1989)CrossRefMATH Gath, I., Geva, A.: Unsupervised optimal fuzzy clustering. IEEE Trans. Pattern Anal. Mach. Intell. 11, 773–780 (1989)CrossRefMATH
14.
Zurück zum Zitat Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31–35, 735–744 (2001). IEEE Hathaway, R.J., Bezdek, J.C.: Fuzzy c-means clustering of incomplete data. IEEE Trans. Syst. Man Cybern. Part B Cybern. 31–35, 735–744 (2001). IEEE
15.
Zurück zum Zitat Hayn, M., et al.: Electricity load profiles in Europe: the importance of household segmentation. Energ. Res. Soc. Sci. 3, 30–45 (2014)CrossRef Hayn, M., et al.: Electricity load profiles in Europe: the importance of household segmentation. Energ. Res. Soc. Sci. 3, 30–45 (2014)CrossRef
16.
Zurück zum Zitat Hernández, L., Baladrón, C., et al.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215 (2012)CrossRef Hernández, L., Baladrón, C., et al.: Classification and clustering of electricity demand patterns in industrial parks. Energies 5(12), 5215 (2012)CrossRef
17.
Zurück zum Zitat Himmelspach, L.: Fuzzy clustering of incomplete data. Ph.D. thesis, Heinrich-Heine-University, Institute of Computer Science (2016) Himmelspach, L.: Fuzzy clustering of incomplete data. Ph.D. thesis, Heinrich-Heine-University, Institute of Computer Science (2016)
18.
Zurück zum Zitat Kolo, A., Kretschmann, C.: Hebung finanzieller Potentiale in der Strombilanzierung. et – Energiewirtschaftliche Tagesfragen 7, 43–45 (2015) Kolo, A., Kretschmann, C.: Hebung finanzieller Potentiale in der Strombilanzierung. et – Energiewirtschaftliche Tagesfragen 7, 43–45 (2015)
19.
Zurück zum Zitat Yang, S.L., Shen, C., et al.: A review of electric load classification in smart grid environment. Renew. Sustain. Energ. Rev. 24, 103–110 (2013) Yang, S.L., Shen, C., et al.: A review of electric load classification in smart grid environment. Renew. Sustain. Energ. Rev. 24, 103–110 (2013)
20.
Zurück zum Zitat López, J.J., Aguado, J.A., et al.: Hopfield-k-means clustering algorithm: a proposal for the segmentation of electricity customers. Electr. Power Syst. Res. 81(2), 716–724 (2011)CrossRef López, J.J., Aguado, J.A., et al.: Hopfield-k-means clustering algorithm: a proposal for the segmentation of electricity customers. Electr. Power Syst. Res. 81(2), 716–724 (2011)CrossRef
21.
Zurück zum Zitat Mahmoudi-Kohan, N., Moghaddam, M.P., Sheikh-El-Eslami, M.: An annual framework for clustering-based pricing for an electricity retailer. Electr. Power Syst. Res. 80(9), 1042–1048 (2010)CrossRef Mahmoudi-Kohan, N., Moghaddam, M.P., Sheikh-El-Eslami, M.: An annual framework for clustering-based pricing for an electricity retailer. Electr. Power Syst. Res. 80(9), 1042–1048 (2010)CrossRef
22.
Zurück zum Zitat Misiti, M., Misiti, Y., et al.: Optimized clusters for disaggregated electricity load forecasting. Revstat 8(2), 105–124 (2010)MathSciNetMATH Misiti, M., Misiti, Y., et al.: Optimized clusters for disaggregated electricity load forecasting. Revstat 8(2), 105–124 (2010)MathSciNetMATH
23.
Zurück zum Zitat Rodrigues, F., Duarte, J., et al.: Proceedings of Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, 5–7 July 2003, pp. 73–85. Springer (2003) Rodrigues, F., Duarte, J., et al.: Proceedings of Machine Learning and Data Mining in Pattern Recognition: Third International Conference, MLDM 2003, Leipzig, Germany, 5–7 July 2003, pp. 73–85. Springer (2003)
24.
Zurück zum Zitat Rudin, C., Waltz, D., et al.: Machine learning for the New York city power grid. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 328–345 (2012)CrossRef Rudin, C., Waltz, D., et al.: Machine learning for the New York city power grid. IEEE Trans. Pattern Anal. Mach. Intell. 34(2), 328–345 (2012)CrossRef
25.
Zurück zum Zitat Räsänen, T., Voukantsis, D., et al.: Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energ. 87(11), 3538–3545 (2010)CrossRef Räsänen, T., Voukantsis, D., et al.: Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data. Appl. Energ. 87(11), 3538–3545 (2010)CrossRef
26.
Zurück zum Zitat Schäfer, H., et al.: Analysing the segmentation of energy consumers using mixed fuzzy clustering. In: Fuzzy Systems (FUZZ-IEEE), pp. 1–7, August 2015 Schäfer, H., et al.: Analysing the segmentation of energy consumers using mixed fuzzy clustering. In: Fuzzy Systems (FUZZ-IEEE), pp. 1–7, August 2015
27.
Zurück zum Zitat Viegas, J.P.L., Vieira, S.M., Sousa, J.M.C.: Fuzzy clustering and prediction of electricity demand based on household characteristics. In: 2015 Conference on International Fuzzy Systems Association and European Society Fuzzy Logic and Technolgy (IFSA-EUSFLAT-15) Viegas, J.P.L., Vieira, S.M., Sousa, J.M.C.: Fuzzy clustering and prediction of electricity demand based on household characteristics. In: 2015 Conference on International Fuzzy Systems Association and European Society Fuzzy Logic and Technolgy (IFSA-EUSFLAT-15)
29.
Zurück zum Zitat Zhang, X., Sun, C.: Dynamic intelligent cleaning model of dirty electric load data. Energ. Conver. Manag. 49(4), 564–569 (2008)CrossRef Zhang, X., Sun, C.: Dynamic intelligent cleaning model of dirty electric load data. Energ. Conver. Manag. 49(4), 564–569 (2008)CrossRef
Metadaten
Titel
Generating Load Profiles Using Smart Metering Time Series
verfasst von
Christian Bock
Copyright-Jahr
2018
DOI
https://doi.org/10.1007/978-3-319-66830-7_20

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