Main objectives of the ICeWater Project
- Demand management systems using metering infrastructure and an analysis of real-time consumption patterns and network operation. Strategies for identification of consumption patterns will permit users segmentation and profiling while mathematical approaches will be adopted for supporting the definition of dynamic pricing policies for effective demand management. Consumer awareness will be increased by making the consumption data, new and alternative tariffs, and the energy costs transparent to the users and stakeholders at a higher granularity.
- Decision support systems which use consumption data and other relevant parameters (e.g. energy costs in the smart electric grid) sampled in real-time (as well as historical data) to enable real-time decision making for the water distribution network operator in order to reduce operational expenses as well as meet the demand for water re-sources. Advanced optimization approaches as well as simulation based approaches shall be used in combination to enable dynamic optimization of the water grid.
- Services for supporting asset management by predicting deterioration (enabling a "fix before break" approach) and providing leak detection and localization functionalities. These services reduce wastage of water and reduce the energy needed for operating the water distribution network. This should also lead to a reduction in the total water consumption to serve a given set of consumers.
- Integration of the above solutions. Components (pumps, water monitoring sensors, etc.) are integrated into an "internet of things". Virtualization of the network and computing resources facilitates sharing among different utility applications. Issues that must be addressed are e.g. the lack of external power for sensors at many locations, underground sensors and underground radio propagation effects.
|This project has received funding from the European Union’s Seventh Framework Programme|
for research, technological development and demonstration under grant agreement no 317624