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.

Single-jet meters are one of the most frequently used domestic meters that can be found in water distribution systems. Like any other water meter technology, they have significant metrological limitations that prevent them, even if recently installed, from measuring all water consumption of a domestic customer. After installation, their metrological characteristics evolve depending on the particular design of the meters and their actual working conditions in the field. This work presents a comprehensive set of tests to determine the initial and after installation weighted error of two types of domestic single-jet water meters. Three non-linear degradation models have been derived from the tests results. These models consider age, totalised volume, or both parameters simultaneously as drivers of the weighted error. The results show that even though the construction of the two examined meters is similar, they have been working under comparable operational conditions and measuring water of the same quality, there is a significant difference in the performance between both types. This result highlights the need to conduct individual analyses for each meter type and the impossibility of generalizing conclusions on how the weighted error could evolve over time.

Accurate water consumption measurement of customers is a crucial component of water utility sustainability. During the last decade, sophisticated measuring technologies without moving components, known as solid-state water meters or static meters, have emerged. Solid-state water meters promise an improved accuracy with more processing and transmission capabilities in comparison with traditional mechanical meters. A compromise needs to be reached between energy consumption and battery life as all these new features are extremely demanding on electric energy. The usual approach adopted by the manufacturer is to reduce the frequency with which static meters take measurements of the circulating flow. This reduction in signal sampling frequency can have a significant effect on the accuracy of the instruments when measuring water consumption events of 30 s or less, these events being common in residential customers. The research presented analyses of the metrological performance of 28 commercially available solid-state water meters from six different manufacturers in the presence of intermittent flows of various durations. The results show that the magnitude and dispersion of the error under intermittent flows is significantly larger in comparison to steady state flow conditions. The ultrasonic meters examined were more influenced by the intermittency than the electromagnetic meters.

One of the main difficulties encountered when designing automatic tools for water end-use identification is the inherent noise present in recorded flow traces. Noise is mainly caused by the inability of the monitoring equipment to accurately register water consumption and data-loggers to register, without distortion, the signal received from the water meter. A universal filtering algorithm has been developed to remove noise and simplify water consumption flow traces with the aim of improving future automatic end-use identification algorithms. The performance of the proposed filtering methodology is assessed through the analysis of 21,647 events. Water consumption data were sourced from two different water end-use studies, having consumers and monitoring equipment with dissimilar characteristics. The results obtained show that the algorithm is capable of removing an average of 70% of the data points that constitute the flow traces of the complex events examined. The simplified flow traces allow for faster and more accurate disaggregation and classification algorithms, without losing significant information or distorting the original signal. The ability of the proposed filtering algorithm to fit the original flow traces was benchmarked using the Kling–Gupta efficiency coefficient, obtaining an average value above 0.79.

El éxito de estrategias para la desagregación y clasificación automática de los consumos de agua en usos finales depende de un adecuado filtrado previo de las trazas de caudal registradas. Se propone un nuevo algoritmo de filtrado, cuyos parámetros de entrada se ajustan mediante un proceso de calibración automático por evento de consumo, asegurando la adaptabilidad y simplificación de la traza filtrada a la original. Esta herramienta se aplica a un caso de estudio mediante el análisis de 5210 eventos de consumo, procedentes de una campaña de monitorización en una ciudad española. Los resultados muestran que el filtro es capaz de simplificar sustancialmente las trazas de caudal manteniendo la información esencial. En media, las trazas de caudal de eventos más complejos pueden definirse con menos del 10% de los puntos de las trazas originales. Además, el análisis realizado permite identificar diversas estrategias para mejorar y optimizar el proceso de filtrado.