Modeling and Clustering Water Demand Patterns from Real-World Smart Meter Data
| Autor: | Zineb Noumir, Véronique Heim, Allou Samé, Nicolas Cheifetz, Cédric Féliers, Anne-Claire Sandraz |
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| Přispěvatelé: | Veolia Eau d'Ile de France, parent, Génie des Réseaux de Transport Terrestres et Informatique Avancée (IFSTTAR/COSYS/GRETTIA), Institut Français des Sciences et Technologies des Transports, de l'Aménagement et des Réseaux (IFSTTAR)-Communauté Université Paris-Est, Syndicat des Eaux d'Ile de France |
| Jazyk: | angličtina |
| Rok vydání: | 2017 |
| Předmět: |
[SPI.OTHER]Engineering Sciences [physics]/Other
Engineering SMART WATER NETWORK Smart meter 0208 environmental biotechnology Posterior probability Context (language use) CLUSTERING 02 engineering and technology computer.software_genre Machine learning lcsh:Technology 7. Clean energy 01 natural sciences lcsh:TD1-1066 010104 statistics & probability WATER DEMAND PATTERN lcsh:Environmental technology. Sanitary engineering 0101 mathematics Cluster analysis Water Science and Technology Civil and Structural Engineering lcsh:T business.industry Pollution 6. Clean water 020801 environmental engineering Generative model Smart grid Artificial intelligence Data mining business computer Automatic meter reading Decomposition of time series |
| Zdroj: | Drinking Water Engineering and Science Drinking Water Engineering and Science, 2017, 2 (10), pp. 75-82. ⟨10.5194/dwes-10-75-2017⟩ Drinking Water Engineering and Science, Vol 10, Pp 75-82 (2017) |
| ISSN: | 1996-9465 |
| DOI: | 10.5194/dwes-10-75-2017⟩ |
| Popis: | Nowadays, drinking water utilities need an acute comprehension of the water demand on their distribution network, in order to efficiently operate the optimization of resources, the management of billing and to propose new customer services. With the emergence of smart grids, based on Automated Meter Reading (AMR), a better understanding of the consumption modes is now accessible for smart cities with more granularities. In this context, this paper evaluates a novel methodology for identifying relevant usage profiles from the water consumption data produced by smart meters. The methodology is fully data-driven using the consumption time series which are seen as functions or curves observed with an hourly time step. First, a Fourier-based additive time series decomposition model is introduced to extract seasonal patterns from time series. These patterns are intended to represent the customer habits in terms of water consumption. Two functional clustering approaches are then used to classify the extracted seasonal patterns: the functional version of K-means, and the Fourier REgression Mixture (FReMix) model. The K-means approach produces a hard segmentation and K representative prototypes. On the other hand, the FReMix is a generative model and produces also K profiles as well as a soft segmentation based on the posterior probabilities. The proposed approach is applied to a smart grid deployed on the largest Water Distribution Network (WDN) in France. The two clustering strategies are evaluated and compared. Finally, a realistic interpretation of the consumption habits is given for each cluster. The extensive experiments and the qualitative interpretation of the resulting clusters allow to highlight the effectiveness of the proposed methodology. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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