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Water Utilities // Smart Solution to Improve Water-Energy Nexus for Water Supply Systems

By Jorge Helmbrechta, Jordi Pastorb and Carolina Moyac. The Water-Energy Nexus is considered as one of the most important multidisciplinary challenges that the global growing water market has to face in the forthcoming years. The current trend of water transmission system to the creation of DMAs (District Metered Areas) offers great possibilities of non-structural solutions that use existing data and transform them into useful information to support decision making. This paper presents a smart solution, developed combining key factors of the energy consumption and the water supply into water networks management to obtain improvements from both water and energy fields.

In the last years, there has been a great interest in the complex relations between energy and water, known as the Water-Energy Nexus. Natural resources, such as energy and water, enable economy growth and support quality of life. Currently, many water systems are not managed sustainably enough. The Smart Metering and the use of large amounts of data from a network enhance the use of software for decision support, but it is not the only way. Smart Solutions can also be applied to networks with less recorded data, which would enhance operators’ knowledge to these data, turn them into useful information for decision-making either for the operation or the maintenance and network design.

The non-structural smart solution presented in this papers increases resource efficiency and environmental performance of water distribution networks by using data acquisition and geographical visualization (real time & historical), weather and water demand forecasting, detection of networks events and hydraulic simulation of the network, and finally through a decision support system based on machine learning (pattern recognition and business rules techniques). The artificial intelligence (AI) provides a flexible way to perform the analysis needed to carry out a holistic management of the network; AI methods described are independent of the monitoring level, the network infrastructure or the water utility specific objectives. They only depend on the knowledge of the network managers and provide a mechanism for maintain and improve management strategies by allowing the addition of variables and rules in the multi-criteria analysis to be performed during the daily operational management.

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Science Direct (open access)

Published

May 2017

In

Science Direct, Procedia Engineering 186 (2017) 101 – 109

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