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• W2895879850 abstract "In the domain of water, companies are deploying innovative infrastructures to instrument buildings with smart meters that collect in particular consumption readings with a fine granularity. Advanced analytics are investigated on these massive and complex data to address different objectives e.g., water consumption forecasting, water leak detection and consumption behavior analysis. This paper is an extension of a previous study aiming to model the dynamics of water consumption time series issued from smart meters in order to predict the future consumption behaviors. We propose a mixture of non-homogeneous hidden Markov models (MixNHMM) to cluster the consumption behavior series in appropriate groups, which share the same transition dynamics, and to forecast the future behaviors in each group of consumers separately. The proposed methodology is applied on a real data gathered during one year and provided by a water utility in France. The experimentation results demonstrate the effectiveness of the proposed approach for the forecasting task. In addition, the clustering capability of the proposed method is confirmed using numeric metrics and visualization." @default.
• W2895879850 created "2018-10-26" @default.
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• W2895879850 date "2018-07-01" @default.
• W2895879850 modified "2023-10-17" @default.
• W2895879850 title "Mixture of Non-homogeneous Hidden Markov Models for Clustering and Prediction of Water Consumption Time Series" @default.
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• W2895879850 doi "https://doi.org/10.1109/ijcnn.2018.8489473" @default.
• W2895879850 hasPublicationYear "2018" @default.
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