This paper presents the energy audit of a water network, which is obtained from the energy equation in integral form, and its time integration extended over a given period (day, month or year). The analysis allows accounting for all the energy in the system, showing that the energy balance is maintained. This balance allows can be used to obtain performance indicators to assess the system from the energetic point of view. From these indicators, it is possible to identify the improvement actions that will make the system more efficient. This energy audit requires a previous water balance and the mathematical model of the network, both of which are necessary to know the energy flows through the system's boundaries.

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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.

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Apparent losses caused by meters inaccuracies can be reduced by analysing meters performance in the water supply and identifying the main causes of inaccuracies. These can have its origin on the meters design and accuracy curves, users' consumption patterns, or specific characteristics of the water supply system.

EPANET is one of the most widely used software packages for water network hydraulic modelling, and is especially interesting for educational and research purposes because it is in the public domain. However, EPANET simulations are demand-driven, and the program does not include a specific functionality to model water leakage, which is pressure-driven. Consequently, users are required to deal with this drawback by themselves. As a general solution for this problem, this paper presents a methodology for including leakage in EPANET models by following a two-stage process. Firstly, leakage is spatially distributed among the nodes, according to the characteristics of the network. Secondly, leakage is modelled through an emitter at each node. The process is described in detail and two numerical examples illustrate the applicability and advantages of the method. In addition, free access through a URL is provided to the leakage modelling tool that has been developed.

Abstract:Calculating the optimum replacement period of meters has always been a major concern for water utility managers. Its determination is time consuming and requires multiple calculations. This paper presents a graphical method to obtain, in a simple but accurate manner, the optimum replacement period of installed meters. For this purpose it has been produced a chart, in which the most influencing variables are considered.These variables include the degradation rate of the weighted error of the meters, the selling price of water, the acquisition and installation cost of the meters, the volume consumed by the users and the discount rate. The chart also allows for a quick sensitivity analysis of different options. For example, by plotting straight lines it is possible to determine by how much the optimum replacement frequency of a meter would change if it degrades at a different rate than expected or the selling price of water increases.