Interactive Water Services: The WATERNOMICS Approach☆

WATERNOMICS focuses on the development of ICT as an enabling technology to manage water as a resource, increase end-user conservation awareness and affect behavioral changes. Unique aspects of WATERNOMICS include personalized feedback about end-user water consumption, the development of systematic and standards-based water resource management systems, new sensor hardware developments, and the introduction of forecasting and fault detection diagnosis to the analysis of water consumption data. These services will be bundled into the WATERNOMICS Water Information Services Platform. This paper presents the overall architectural approach to WATERNOMICS and details the potential interactive services possible based on this novel platform.

[1]  Tatsuo Nakajima,et al.  Family interaction for responsible natural resource consumption , 2012, CHI EA '12.

[2]  Dimitri P. Bertsekas,et al.  Dynamic Programming and Optimal Control, Two Volume Set , 1995 .

[3]  Edward Curry,et al.  Enterprise energy management using a linked dataspace for Energy Intelligence , 2012, 2012 Sustainable Internet and ICT for Sustainability (SustainIT).

[4]  Dominic T. J. O'Sullivan,et al.  Development of an Online Expert Rule Based Automated fault Detection and Diagnostic (AFDD) Tool for Air Handling Units: Beta Test Results , 2013 .

[5]  Edward Curry,et al.  Linking building data in the cloud: Integrating cross-domain building data using linked data , 2013, Adv. Eng. Informatics.

[6]  Other.  Towards efficient use of water resources in Europe , 2012 .

[7]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[8]  Roland K. Price,et al.  Ensemble Precipitation and Water-Level Forecasts for Anticipatory Water-System Control , 2008 .

[9]  Andrea Emilio Rizzoli,et al.  TwoLe: a software tool for planning and management of water reservoir networks , 1999 .

[10]  Philippe Pasquier,et al.  Elements of Consumption: An Abstract Visualization of Household Consumption , 2011, Smart Graphics.

[11]  Dirk Schwanenberg,et al.  Tree structure generation from ensemble forecasts for real time control , 2013 .

[12]  Karl Johan Åström,et al.  PID Controllers: Theory, Design, and Tuning , 1995 .

[13]  Peter-Jules van Overloop,et al.  Multiple Model Predictive Control on a drainage canal system , 2008 .

[14]  Edward Curry,et al.  Linked Water Data For Water Information Management , 2014 .

[15]  Congli Dong,et al.  Scenario development for water resource planning and management: A review , 2013 .

[16]  Dominic T. J. O'Sullivan,et al.  Development and alpha testing of a cloud based automated fault detection and diagnosis tool for Air Handling Units , 2014 .

[17]  Newsha K. Ajami,et al.  Hydrologic ensemble prediction experiment focuses on reliable forecasts , 2005 .

[18]  Jean-Luc de Kok,et al.  Delineating the Model-Stakeholder Gap: Framing Perceptions to Analyse the Information Requirement in River Management , 2009 .