Abstract
The ongoing deployment of Automated Meter Reading systems (AMR) in the European electricity industry has created new challenges for electricity utilities in terms of how to fully utilise the wealth of timely measured AMR data, not only to enhance day-to-day operations, but also to facilitate demand response programs. In this study we investigate a visual data mining approach for decision-making support with respect to pricing differentiation or designing demand response tariffs. We cluster the customers in our sample according to the customers’ actual consumption behaviour in 2009, and profile their electricity consumption with a focus on the comparison of two sets of seasonal and time based variables. The results suggest that such an analytical approach can visualise deviations and granular information in consumption patterns, allowing the electricity companies to gain better knowledge about the customers’ electricity usage. The investigated electricity consumption time series profiling approach will add empirical understanding of the problem domain to the related research community and to the future practice of the energy industry.
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
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Abdel-Aal, R.E.: Short Term Hourly Load Forecasting Using Abductive Networks. IEEE Transactions on Power Systems 19(1), 164–173 (2004)
Back, B., Toivonen, J., Vanharanta, H., Visa, A.: Comparing numerical data and text information from annual reports using self-organizing maps. International Journal of Accounting Information Systems 2(4), 249–269 (2001)
Baragoin, C., Andersen, C., Bayerl, S., Bent, G., Lee, J., Schommer, C.: Mining Your Own Business in Retail Using DB2 Intelligent Miner for Data. IBM Redbooks (2001)
Berry, M.J.A., Linoff, G.: Data mining techniques: for marketing, sales, and customer relationship management. Wiley Computer Publishing (2004)
Bishop, C.M.: Neural Networks for Pattern Recognition. Oxford University Press, Avon (1995)
CEER Advice on the take-off of a demand response electricity market with smart meters, Ref: C11-RMF-36-03 (December 2011)
Charytoniuk, W., Chen, M.-S.: Very Short Term Load Forecasting Using Artificial Neural Networks. IEEE Transactions on Power Systems 15(1), 363–368 (2000)
Collica, R.S.: CRM Segmentation and Clustering Using SAS Enterprise Miner. SAS Publishing (2007)
Cotti, M., Millan, R.: Cervantes project and ’meters and more’: the state of the art of smart metering implementation in Europe. In: Proceeding of 21st International Conference on Electricity Distribution (CIRED 2011), Frankfurt, Germany, paper 0829 (2011)
Deboeck, G., Kohonen, T.: Visual explorations in finance using self-organizing maps. Springer, Berlin (1998)
Eklund, T., Back, B., Vanharanta, H., Visa, A.: Using the self-organizing map as a visualization tool in financial benchmarking. Information Visualization 2(3), 171–181 (2003)
Garpetun, L.: Experiences from operations after a full-scale smart metering rollout regarding availability and reliability. In: Proceedings of the 21st International Conference on Electricity Distribution (CIRED 2011), Frankfurt, Germany, paper 0415 (2011)
Han, J., Kamber, M.: Data Mining: Concepts and Techniques. The Morgan Kaufmann Series in Data Management Systems. Morgan Kaufmann (2000)
Kaski, S., Kangas, J., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers 1981-1997. Neural Computing Surveys 1, 102–350 (1998)
Keppo, J., Räsänen, M.: Pricing of electricity tariffs in competitive markets. Energy Economics 21, 213–223 (1999)
Kohonen, T.: Self-organizing maps. Springer Series in Information Sciences, vol. 4, pp. 22–25. Springer, Berlin (1997)
Kohonen, T.: Self-organizing maps, 3rd edn. Springer, Berlin (2001)
Kohonen, T., Hynninen, J., Kangas, J., Laaksonen, J.: SOM PAK: The self-organizing map program package. Technical Report A31, Helsinki University of Technology, Laboratory of Computer and Information Science (1996)
Lee, S.-C., Gu, J.-C., Suh, Y.-H.: A Comparative Analysis of Clustering Methodology and Application for Market Segmentation: K-Means, SOM and a Two-Level SOM. Foundations of Intelligent Systems, 435–444 (2006)
Lendasse, A., Lee, J., Wertz, V., Verleysen, M.: Forecasting electricity consumption using nonliner projection and self-organizing maps. Neurocomputing 48, 299–311 (2002)
Liu, H., Eklund, T., Back, B., Vanharanta, H.: Visual Data Mining: Using Self-Organizing Maps for Electricity Distribution Regulation. In: Ariwa, E., El-Qawasmeh, E. (eds.) DEIS 2011. CCIS, vol. 194, pp. 631–645. Springer, Heidelberg (2011)
Mutanen, A., Repo, S., Järventausta, P.: AMR in distribution state estimation. In: Proceedings of Nordic Distribution and Asset Management Conference, Bergen, Norway (2008)
Mutanen, A., Repo, S., Järventausta, P.: Customer Classification and Load Profiling Based on AMR Measurements. In: Proceedings of the 21st International Conference on Electricity Distribution (CIRED 2011), Frankfurt, Germany, paper 0277 (2010)
Nababhushana, T.N., Veeramanju, K.T., Shivanna: Coherency identification using growing self organizing feature maps (power system stability). In: IEEE Proceedings of EMPD 1998. International Conference on Energy Management and Power Delivery, vol. 1, pp. 113–116 (1998)
Oja, M., Kaski, S., Kohonen, T.: Bibliography of Self-Organizing Map (SOM) Papers: 1998-2001 Addendum. Neural Computing Surveys 3, 1–156 (2002)
Rehtanz, C.: Visualisation of voltage stability in large electric power systems. In: IEEE Proceedings Generation, Transmission and Distribution, vol. 146, pp. 573–576 (1999)
Riqueline, J., Martinez, J.L., Gomez, A., Goma, D.C.: Possibilities of artificial neural networks in short-term load forecasting. In: Proceedings of the IASTED International Conference Power and Energy Systems, pp. 165–170. IASTED/ACTA Press, Anaheim (2000)
Sarlin, P., Peltonen, T.: Mapping the State of Financial Stability. ECB WP No. 1382/2011 (2011)
Samarasinghe, S.: Neural networks for applied sciences and engineering: from fundamentals to complex pattern recognition. CRC Press (2007)
Valtonen, P., Honkapuro, S., Partanen, J.: Improving Short-Term Load Forecasting Accuracy by Utilizing Smart Metering. In: Proceedings of the 20 th International Conference on Electricity Distribution (CIRED 2010), Lyon, France, paper 0056 (2010a)
Vesanto, J., Alhoniemi, E.: Clustering of the self-organizing map. IEEE Transactions on Neural Networks 11(3), 586–600 (2000)
Ward, J.H.: Hierarchical grouping to optimize an objective function. Journal of the American Statistical Association 58(301), 236–244 (1963)
Wiskott, L., Sejnowski, T.: Constrained optimization for neural map formation: A unifying framework for weight growth and normalization. Neural Computation 10(3), 671–716 (1998)
Yao, Z., Holmbom, A.H., Eklund, T., Back, B.: Combining Unsupervised and Supervised Data Mining Techniques for Conducting Customer Portfolio Analysis. In: Perner, P. (ed.) ICDM 2010. LNCS, vol. 6171, pp. 292–307. Springer, Heidelberg (2010)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Liu, H., Yao, Z., Eklund, T., Back, B. (2012). Electricity Consumption Time Series Profiling: A Data Mining Application in Energy Industry. In: Perner, P. (eds) Advances in Data Mining. Applications and Theoretical Aspects. ICDM 2012. Lecture Notes in Computer Science(), vol 7377. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-31488-9_5
Download citation
DOI: https://doi.org/10.1007/978-3-642-31488-9_5
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-31487-2
Online ISBN: 978-3-642-31488-9
eBook Packages: Computer ScienceComputer Science (R0)