Customer Clustering of French Transmission System Operator (RTE) Based on Their Electricity Consumption
| Autor: | Hoai Minh Le, Bach Tran, Hoai An Le Thi, Gabriel Da Silva, Vincent Lefieux |
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| Přispěvatelé: | Laboratoire de Génie Informatique, de Production et de Maintenance (LGIPM), Université de Lorraine (UL) |
| Rok vydání: | 2019 |
| Předmět: |
Consumption (economics)
Schedule Dynamic time warping 021103 operations research Computer science 0211 other engineering and technologies 02 engineering and technology computer.software_genre Similarity (network science) 020204 information systems Scalability 0202 electrical engineering electronic engineering information engineering Embedding [INFO]Computer Science [cs] Data mining Convex function Cluster analysis computer ComputingMilieux_MISCELLANEOUS |
| Zdroj: | Advances in Intelligent Systems and Computing ISBN: 9783030218027 WCGO Optimization of Complex Systems: Theory, Models, Algorithms and Applications. Advances in Intelligent Systems and Computing 6th World Congress on Global Optimization, WCGO 2019 Optimization of Complex Systems: Theory, Models, Algorithms and Applications. Advances in Intelligent Systems and Computing, 991, pp.893-905, 2020, ⟨10.1007/978-3-030-21803-4_89⟩ |
| DOI: | 10.1007/978-3-030-21803-4_89 |
| Popis: | We develop an efficient approach for customer clustering of French transmission system operator (RTE) based on their electricity consumption. The ultimate goal of customer clustering is to automatically detect patterns for understanding the behaviors of customers in their evolution. It will allow RTE to better know its customers and consequently to propose them more adequate services, to optimize the maintenance schedule, to reduce costs, etc. We tackle three crucial issues in high-dimensional time-series data clustering for pattern discovery: appropriate similarity measures, efficient procedures for high-dimensional setting, and fast/scalable clustering algorithms. For that purpose, we use the DTW (Dynamic Time Warping) distance in the original time-series data space, the t-distributed stochastic neighbor embedding (t-SNE) method to transform the high-dimensional time-series data into a lower dimensional space, and DCA (Difference of Convex functions Algorithm) based clustering algorithms. The numerical results on real-data of RTE’s customer have shown that our clutering result is coherent: customers in the same group have similar consumption curves and the dissimilarity between customers of different groups are quite clear. Furthermore, our method is able to detect whether or not a customer changes his way of consuming. |
| Databáze: | OpenAIRE |
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