Automated Household Water End-Use Disaggregation through Rule-Based Methodology

Mazzoni, Filippo; Alvisi, Stefano; Franchini, Marco; Ferraris, Marco; Kapelan, Zoran

doi:10.1061/(asce)wr.1943-5452.0001379

Cited by 11 publications

(4 citation statements)

References 28 publications

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“…Gourmelon et al (2021) evaluated the efficacy of several machine learning techniques in predicting downstream events via simulated smart meter data. Mazzoni et al (2021) on the other hand used intrusive data read from four households to disaggregate end-use features from an upstream smart meter via machine learning. Finally, Meyer et al (2021) employed machine learning to determine whether an upstream water event, recorded on a smart meter, indicated an indoor or outdoor end-use event.…”

Section: Introductionmentioning

confidence: 99%

A machine learning framework for predicting downstream water end-use events with upstream sensors

et al. 2022

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Understanding the end-use of water is essential to a plethora of critical research in premise plumbing. However, direct access to end-use data through physical sensors is prohibitively expensive for most researchers, building owners, operators, and practitioners. Therefore, machine learning models can alleviate these costs by predicting downstream end-use events (e.g., sink, shower, dishwasher, and washing machine) via an affordable subset of upstream sensors. Choosing which upstream sensors, as well as data preprocessing methods, are best for machine learning has historically been a manual process. This paper proposes a novel approach to systematically configure the machine platform automatically. The optima were determined through a Pareto analysis of the exhaustive combinations of upstream predictors and preprocessing methods. The model was trained and validated with real-world data obtained from a house that has been extensively monitored for over a year. Results from the analysis suggested that downstream events can be effectively predicted with minimum overfitting error for most categories, using as few as two to four upstream sensors. This study automatically implemented highly accurate machine learning models to predict downstream features within premise plumbing systems, significantly lowering the costs of researching residential plumbing best practices such as water conservation.

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Section: Introductionmentioning

confidence: 99%

A machine learning framework for predicting downstream water end-use events with upstream sensors

et al. 2022

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“…In this work, we consider the problem of non-intrusive water usage monitoring in households as our case study [13]. As water scarcity is increasingly affecting the world, the development of new strategies focusing on water conservation has become crucial.…”

Section: Introductionmentioning

confidence: 99%

“…This method achieved a 91% score on classifying water end-uses using a limited dataset of flow measurement from one point in a single house. The authors in [13] propose a rule-based methodology for end-use disaggregation, by prioritizing the identification of appliances with comparably regular behavior. The methodology was applied to a sample of four households in Italy where detailed water-use data were collected at the inlet point and at each end-use over a period of 2 months.…”

Section: Introductionmentioning

confidence: 99%

A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Papatheodoulou

Pavlou

Vrachimis

et al. 2022

IFIP Advances in Information and Communication Technology

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Numerous real-world problems from a diverse set of application areas exist that exhibit temporal dependencies. We focus on a specific type of time series classification which we refer to as aggregated time series classification. We consider an aggregated sequence of a multi-variate time series, and propose a methodology to make predictions based solely on the aggregated information. As a case study, we apply our methodology to the challenging problem of household water end-use dissagregation when using non-intrusive water monitoring. Our methodology does not require a-priori identification of events, and to our knowledge, it is considered for the first time. We conduct an extensive experimental study using a residential water-use simulator, involving different machine learning classifiers, multi-label classification methods, and successfully demonstrate the effectiveness of our methodology.

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“…The authors in [15] proposed the use of an adaptable neuro-fuzzy network to classify water end-uses achieving high accuracy, using a limited dataset of flow measurements. In more recent studies, machine learning and data analytic algorithms were developed to address the problem of water end-use disaggregation, with promising results [16][17][18][19][20][21]. Several drawbacks that were noted in these studies include the need for a large amount of historical data to train the model and the absence of disaggregation techniques for combined water events.…”

Section: Introductionmentioning

confidence: 99%

Non-intrusive water usage classification considering limited training data

Pavlou,

Vrachimis,

Eliades

et al. 2022

Proceedings - 2nd International Join Conference on Water Distribution System Analysis (WDSA)& Computing and Control in the

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Smart metering of domestic water consumption to continuously monitor the usage of different appliances has been shown to have an impact on people’s behavior towards water conservation. However, the installation of multiple sensors to monitor each appliance currently has a high initial cost and as a result, monitoring consumption from different appliances using sensors is not cost-effective. To address this challenge, studies have focused on analyzing measurements of the total domestic consumption using Machine Learning (ML) methods, to disaggregate water usage into each appliance. Identifying which appliances are in use through ML is challenging since their operation may be overlapping, while specific appliances may operate with intermittent flow, making individual consumption events hard to distinguish. Moreover, ML approaches require large amounts of labeled input data to train their models, which are typically not available for a single household, while usage characteristics may vary in different regions. In this work, we initially propose a data model that generates synthetic time series based on regional water usage characteristics and resolution to overcome the need for a large training dataset with real labeled data. The method requires a small number of real labeled data from the studied region. Following this, we propose a new algorithm for classifying single and overlapping household water usage events, using the total domestic consumption measurements. The classification procedure is described below: 1) During the offline feature learning stage, a dataset of labeled data corresponding to water-use profile signals is analyzed to some predefined features, such as event volume, event duration, event flow peak, and event signature, to extract its statistical properties, 2) The event classification stage monitors the provided measurement time-series for events between zero-flow intervals. The identified events are then classified using Dynamic Time Wrapping and an optimization procedure that finds the best label for the observed event based on the features learned in the first stage and similarity indices. Non-classified events are processed using a variation vector technique to identify the combined events which are then split into sub-single events and classified.Extended version of this article is selected for possible publication in a Special Issue in the Journal of Hydroinformatics.

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Automated Household Water End-Use Disaggregation through Rule-Based Methodology

Cited by 11 publications

References 28 publications

A machine learning framework for predicting downstream water end-use events with upstream sensors

A machine learning framework for predicting downstream water end-use events with upstream sensors

A Multi-label Time Series Classification Approach for Non-intrusive Water End-Use Monitoring

Non-intrusive water usage classification considering limited training data

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