Water End Use Disaggregation Based on Soft Computing Techniques

Abstract

Disaggregating residential water end use events through the available commercial tools needs a great investment in time to manually process smart metering data. Therefore, it is extremely difficult to achieve a homogenous and sufficiently large corpus of classified single-use events capable of accurately describe residential water consumption. The main goal of the present paper is to develop an automatic tool that facilitates the disaggregation of the individual water consumptions events from the raw flow trace. The proposed disaggregation methodology is conducted through two actions that are iteratively performed: first, the use of an advanced two-step filter, whose calibration is automatically conducted by the Elitist Non-Dominated Sorting Genetic Algorithm NSGA-II; and second, a cropping algorithm based on the filtered water consumption flow traces. As a secondary goal, yet complementary to the main one, a semiautomatic massive classification process has been developed, so that the resulting single-use events can be easily categorized in the different water end uses in a household. This methodology was tested using water consumption data from two different case studies. The characteristics of the households taken as reference and their occupants were unequivocally dissimilar from each other. In addition, the monitoring equipment used to obtain the consumption flow traces had completely different technical specifications. The results obtained from the processing of the two studies show that the automatic disaggregation is both robust and accurate, and produces significant time saving compared to the standard manual analysis.

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

“…This approach utilises intelligent machine learning algorithms for the end use disaggregation task. Some of notable studies includes (Pastor-Jabaloyes et al 2018), (Cardell-Oliver et al 2016), BuntBrain-ForEndUses® by (Arregui 2015), REU2016 by Webber 2016, 2018), SmartH20 by (Cominola et al 2015 and Autoflow by (Nguyen et al 2015;Stewart et al 2018). The latter mentioned Autoflow software uses a combination of different pattern recognition and data mining techniques including HMM, ANN, DTW, histogram analysis and time-of-day probability function to automate the end use analysis process.…”

Enhancing Residential Water End Use Pattern Recognition Accuracy Using Self-Organizing Maps and K-Means Clustering Techniques: Autoflow v3.1

“…The existing water end-use model predominately applied only computing techniques [43]. The entire procedure for the hybrid technique includes two stages.…”

Advanced Hydroinformatic Techniques for the Simulation and Analysis of Water Supply and Distribution Systems

“…This issue contains 18 papers which focus on some of the mentioned problems of water distribution system management. The key points are: (i) design of water system [1][2][3][4]; (ii) optimization of network performance assessment [5][6][7][8]; (iii) monitoring and diagnosis of pressure pipe system [9][10][11]; (iv) optimal water quality management [12][13][14]; and (v) modelling and forecasting of water demand [15][16][17][18].…”

“…Secondly, Pastor-Jabaloyes at al. [16] present an automatic tool for smart metered water demand time series disaggregation into single-use events. The tool is based on a filter automatically calibrated by using NSGA-II algorithm, and on a cropping algorithm.…”