Projects

UWMH focuses on Research and Innovation activities undertaken within the framework of National and European Research and Development projects and find their way into practice through the provision of consultancy services

E3WDM

DURATION: 01.05.2015 to 30.11.2018

BUDGET: 1,600,000.00 NOK/ 8,000,000.00 NOK

FUNDING: RESEARCH PROJECT funded by Norwegian Research Council & City of OSLO

ABOUT E3WDM

E3WDM: EFFICIENT, EFFECTIVE, ECONOMIC WATER DEMAND MANAGEMENT IN THE GROWING OSLO CITY, was a two year research project funded by the City of Oslo and the Norwegian Research Council. The project aimed to ensure efficient, effective and economic water demand management in the growing city of Oslo. The consortium worked closely as one Water industry cluster, including the water utility of Oslo, research Institutions (SINTEF, IDEA RT, NTUA) and private companies located in Oslo and known on national level (KAMSTRUP, LKI,POWEL).

OBJECTIVES

Key objectives of the project were the:

  • Development of the smart distribution network model that integrated hydraulic modeling, Remote Real Time Control and Information and Communication Technology tailored to the challenges of the Oslo network (pilot level);
  • Implementation and test of the use of smart water meters (pilot level);
  • Development of smart meter data visualization online platforms (dashboards);
  • Development of appropriate analytics for best use of data from smart water meters (pilot level);
  • Provision of a best practice protocol for water consumption calculation;
  • Development of an integrated model for Oslo to be used to upscale pilot results and to model alternative solutions for efficient, effective, economic water demand management (city level);
  • Provision of the implementations plan for selected solutions.
 
OUR ROLE IN THE PROJECT

In the framework of E3WDM, our focus was on the development of methodologies and tools to advance the extraction of insights from the analysis of large water demand records as obtained from the smart water meters at the household level. Specifically, our focus was on the massive analysis of distributional and stochastic (dependence) properties of water demand at multiple fine temporal scales and at the level of household. In parallel, we developed a new web platform, based on the in-house Nessie system, to allow the householders to monitor (in real-time), understand and manage the water demand of their households.

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