Nowadays, a large number of water utilities still manage their operation on the instant water demand of the network, meaning that the use of the equipment is conditioned by the immediate water necessity. The water reservoirs of the networks are filled using pumps that start working when the water level reaches a specified minimum, stopping when it reaches a maximum level. Shifting the focus to water management based on future demand allows use of the equipment when energy is cheaper, taking advantage of the electricity tariff in action, thus bringing significant financial savings over time. Short-term water demand forecasting is a crucial step to support decision making regarding the equipment operation management. For this purpose, forecasting methodologies are analyzed and implemented. Several machine learning methods, such as neural networks, random forests, support vector machines and k-nearest neighbors, are evaluated using real data from two Portuguese water utilities. Moreover, the influence of factors such as weather, seasonality, amount of data used in training and forecast window is also analysed. A weighted parallel strategy that gathers the advantages of the different machine learning techniques is suggested. The results are validated and compared with those achieved by autoregressive integrated moving average (ARIMA) also using benchmarks.

Water utilities collect, store and manage a vast set of data using a large set of information systems (IS). For Infrastructure Asset Management (IAM) planning those data need to be processed and transformed into information. However, information management efficiency often falls short of desired results. This happens particularly in municipalities where management is structured according to local government model conventions. Besides the existing IS at utilities' disposal, engineers and managers take their decisions based on information that is often incomplete, inaccurate or out-of-date. One of the main challenges faced by asset managers is integrating the several, often conflicting, sources of information available on the infrastructure, its condition and performance, and the various predictive analyses that can assist in prioritizing projects or interventions. This paper presents an overview of the IS used by Portuguese water utilities and discusses how data from different IS can be integrated in order to support IAM.

This paper presents a set of computational tools specially developed for supporting the operation and management of water distribution systems towards digital transformation of water services. These tools were developed in the scope of two R&D projects carried out in Portugal, DECIdE and WISDom, during 2018–2022. The DECIdE project focused on the development of tools for importing cadastral and operational data, as well as on the three operational tools for supporting the performance assessment: the first allows the calculation of different key performance indicators, both at a global and sectorial level, which is an annual requirement of the water regulator, and the other two allow the calculation of the water and the energy balances and a set of complementary indices. The WISDom project aimed at the implementation of applications that directly address specific water utility needs, namely, the flow rate data processing, the optimal location of pressure sensors, the identification of critical areas in the distribution network for pipe burst location, and the prioritization of pipes for rehabilitation. Implemented tools are useful to support water utilities in the daily operation and management of their systems, being a step forward towards digital transformation of the water sector.

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