Predictive Classification of Water Consumption Time Series Using Non-homogeneous Markov Models

Abadi, Milad Leyli; Samé, Allou; Oukhellou, Latifa; Cheifetz, Nicolas; Mandel, Pierre; Feliers, Cédric; Chesneau, O.

doi:10.1109/dsaa.2017.32

Cited by 17 publications

(10 citation statements)

References 18 publications

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“…After regression and ANN-based techniques, the application of stochastic-based techniques is notable for forecasting water demand. In several studies [29,61,62,64,68], researchers have applied different stochastic approaches.…”

Section: Stochastic-based Methodsmentioning

confidence: 99%

“…Abadi et al [64] proposed a mixture of nonhomogeneous hidden Markov models (MixJNHMM) to cluster and forecast short-term water demand. The aim of this study was to cluster consumption behaviour series into several groups and forecast future behaviours for each group of consumers separately.…”

Section: Stochastic-based Methodsmentioning

confidence: 99%

“…In a 2010 study, categorising water end-use was identified as the most critical challenge and one that required urgent attention, because existing approaches to water end-use analysis required much time [64]. Since then, several approaches have been proposed [4,5,64,89] to classify water end-use. These can be placed into three broad categories: decision tree methods; data mining techniques applied to data collected from sensor devices integrated into water appliances; and hybrid approaches.…”

Section: Water End-use Classificationmentioning

confidence: 99%

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Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

Rahim

Nguyen

Stewart

et al. 2020

Water

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Digital or intelligent water meters are being rolled out globally as a crucial component in improving urban water management. This is because of their ability to frequently send water consumption information electronically and later utilise the information to generate insights or provide feedback to consumers. Recent advances in machine learning (ML) and data analytic (DA) technologies have provided the opportunity to more effectively utilise the vast amount of data generated by these meters. Several studies have been conducted to promote water conservation by analysing the data generated by digital meters and providing feedback to consumers and water utilities. The purpose of this review was to inform scholars and practitioners about the contributions and limitations of ML and DA techniques by critically analysing the relevant literature. We categorised studies into five main themes: (1) water demand forecasting; (2) socioeconomic analysis; (3) behaviour analysis; (4) water event categorisation; and (5) water-use feedback. The review identified significant research gaps in terms of the adoption of advanced ML and DA techniques, which could potentially lead to water savings and more efficient demand management. We concluded that further investigations are required into highly personalised feedback systems, such as recommender systems, to promote water-conscious behaviour. In addition, advanced data management solutions, effective user profiles, and the clustering of consumers based on their profiles require more attention to promote water-conscious behaviours.

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Section: Stochastic-based Methodsmentioning

confidence: 99%

Section: Stochastic-based Methodsmentioning

confidence: 99%

Section: Water End-use Classificationmentioning

confidence: 99%

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Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

Rahim

Nguyen

Stewart

et al. 2020

Water

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“…Following the line of research in [33,34], we are currently engaged in work on this topic based on machine learning models such as non-homogeneous Markov models [35] where forecasting uses the clustering results.…”

Section: Discussionmentioning

confidence: 99%

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Melzi

Samé²,

Zayani

et al. 2017

Energies

Self Cite

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Abstract:The large amount of data collected by smart meters is a valuable resource that can be used to better understand consumer behavior and optimize electricity consumption in cities. This paper presents an unsupervised classification approach for extracting typical consumption patterns from data generated by smart electric meters. The proposed approach is based on a constrained Gaussian mixture model whose parameters vary according to the day type (weekday, Saturday or Sunday). The proposed methodology is applied to a real dataset of Irish households collected by smart meters over one year. For each cluster, the model provides three consumption profiles that depend on the day type. In the first instance, the model is applied on the electricity consumption of users during one month to extract groups of consumers who exhibit similar consumption behaviors. The clustering results are then crossed with contextual variables available for the households to show the close links between electricity consumption and household socio-economic characteristics. At the second instance, the evolution of the consumer behavior from one month to another is assessed through variations of cluster sizes over time. The results show that the consumer behavior evolves over time depending on the contextual variables such as temperature fluctuations and calendar events.

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“…Some of these are given below. Abadi et al (2017) forecasted the dynamics of water consumption behavior and predicted future consumption behavior with a new predictive approach based on non-homogenous Markov Models. This predictive classification method was applied on a real dataset provided by a water supply enterprise in France, and the results show that it can be useful for water supply enterprises to better manage water resources and respond to consumer needs.…”

Section: Literature Reviewmentioning

confidence: 99%

Examining of the countries in terms of water resources along with the effect of climate change by time series clustering

Akay¹

2021

Preprint

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Today, the problem of climate change is addressed in many ways. The most important effect of climate change is the disruption of the water cycle. Therefore, the location and timing of the water resources in the world are changing. Water is an indispensable resource that connects all living things together and directly affects their lives. Water is not only a biological requirement for man, but also for economic, social, and cultural life itself. However, this resource, which is of vital importance, exists unfortunately in a limited amount on earth. This study aims to identify similar countries in terms of water resources by taking into account the changes in precipitation amounts together with climate change. For this purpose, a time series clustering analysis was conducted to precipitation amounts of 21 countries between 2009-2017 with the Hierarchical clustering and Partitioning Around Medoids (PAM) clustering methods. Countries are divided into two clusters and they were evaluated according to their water risk levels and populations. It should be noted that countries with low precipitation, high risk of water, and a high population should take urgent measures for climate change.

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Predictive Classification of Water Consumption Time Series Using Non-homogeneous Markov Models

Cited by 17 publications

References 18 publications

Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

Machine Learning and Data Analytic Techniques in Digital Water Metering: A Review

A Dedicated Mixture Model for Clustering Smart Meter Data: Identification and Analysis of Electricity Consumption Behaviors

Examining of the countries in terms of water resources along with the effect of climate change by time series clustering

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