An adaptive model for the autonomous monitoring and management of water end use

Abstract

Most pattern classification systems are usually developed based on the training of historical data, and as a result, the performance of these models relies heavily on the amount of collected information. However, in many cases, such data collection process is relatively costly, which eventually limits the efficiency as well as the widespread implementation of the final developed model. In this context, the paper focuses on presenting an advanced universal water management system, which could interface with both water consumers and utilities via smart phone and web application. Originally, Autoflow©, a prototype tool that is used to disaggregate total water consumption into each end-use category was developed, which achieved an accuracy ranging from 74 to 94%. However, a drawback of this model was that it was trained with data collected from only Australia; therefore, accuracy reductions would likely be observed when this system is implemented in different countries having very different water using appliances and behaviour patterns. To avoid the costly data collection process for model calibration when operating in new regions, this research study introduces an enhanced model, namely Autoflow U (i.e. U stands for Universal). This new tool can be applied in residential properties globally to autonomously disaggregate water consumption into the seven main water end-use categories, namely: shower, toilet, tap, clothes washer, dishwasher, evaporative air cooler and irrigation, without the need for collecting new regional end-use data for model calibration. In order to develop this new tool, Decision Trees, Dynamic Time Warping (DTW), Self Organising Map (SOM) and Hidden Markov Model (HMM) techniques were utilised. The test results obtained from 230 properties in both Australia and the US showed that the Autoflow U achieved 72-93% accuracy.

Implications of Experiment Set-Ups for Residential Water End-Use Classification

“…Recently, deep neural networks have achieved tremendous success in various disciplines, such as medical image processing, computer vision, space or geoscience. Although artificial neural networks have been applied for end-use classification with promising results (refer to [11][12][13][14]), the networks employed as classifiers still have a shallow structure. Sophisticated neural networks in combination with deep learning techniques may benefit the entire end-use classification process comprising data preprocessing, feature extraction and classification.…”

“…Later, this approach was extended with a hybrid method consisting of Self-Organizing Maps and a k-means algorithm in the subsequent publications [3,4]. Most supervised approaches are based on Hidden Markov Models and a subsequent optimization [5][6][7][8][9][10][11][12][13][14][15][16], but Artificial Neural Networks [9][10][11][12][13], Decision Trees [14] and Multi-category Robust Linear Programming [17] are used as well. Support Vector Machines (SVMs) were employed by Vitter et al [18] to categorize water events using water consumption data and coincident electricity data.…”

Overcoming data scarcity in flood hazard assessment using remote sensing and artificial neural network

“…The Artificial Neural Network (ANN) is a useful tool to apply pattern recognition (classification) in engineering and scientific applications such as biology, medical science and remote sensing technology (Nguyen et al 2018). This study utilizes the strength of pattern recognition in ANN to correct/improve the elevation in SRTM.…”

Is smart water meter temporal resolution a limiting factor to residential water end-use classification? A quantitative experimental analysis

“…Conversely, smart (or digital) water meters enable the collection and automated reporting of fine resolution water use data, thereby allowing planners and utilities to better understand demand patterns and enact management strategies. Smart metering can help the development of accurate demand characterization and forecasts and, hence, improve the operation and long-term planning of water supply and distribution systems (Sønderlund et al 2016, Stewart et al 2018, or promote durable conservation behaviors (Cominola et al 2021). In addition, detailed knowledge about water consumption at the household level can also translate into financial savings for home occupants, especially when complemented with information about individual end uses (e.g., Blokker et al 2010).…”