NAIADES: A holistic water ecosystem for digitisation of urban water sector

Abstract:
Ecosystem envisions transforming water sector through automated and smarter water resource management and environmental monitoring, achieving a high level of water services in both residential or commercial consumers, exploiting the efficient use of physical and digital components of water ecosystem.

NAIADES will apply in various situations on diverse big data in terms of sources, data types, and entities represented that is collected by such water monitoring and control systems in Europe, including (i) the water consumption in both retail and corporation efficiency, (ii) the confidence of water consumers (including special groups as ageing, disabled persons and children), by measuring the water quality in residential buildings, offices and public infrastructures (mall, hospital), (iii) the safety and reliability through the detection of warning signs from equipment failures and maintenance report, and (iv) personalized persuasive feedback and recommendation services provided to the NAIADES App Users aiming to enhance public awareness on water consumption and usage savings, and promote user engagement in water conservation activities. NAIADES AI elements, aim to provide multidimensional intelligence on the water ecosystem by introducing: Situational Intelligence - by collecting real-time data from the buildings as they are in operation and analysing it in three different dimensions; Spatial, Temporal and Nodal; Operational intelligence - by using the power of data and its capability to extract the right information at the right time to provide insight into water infrastructure operation and improve the effectiveness of maintenance activities; Asset intelligence: the continuous data streams produced from various sub-systems in buildings will help OEMs build digital twins that represent physical systems in real-time. NAIADES ecosystem will be validated and demonstrated in three heterogeneous pilot sites.

This project has received funding from the European Union’s Horizon 2020 research and innovation program under grant agreement No 820985