What is the challenge that is being addressed?

Improvement of the asset management and the operational efficiency of the local smart distribution grid, as well the deployment of cross-network coordinated management and operation with water networks. Optimisation of yearly expenditures for operation and maintenance by advanced remote control of the asset. 

What is the AI solution the project plans to implement?

Third pilot scenario will be aimed to demonstrate and validate other AI-based energy-driven and non-energy services, which leverage on the massive deployment and availability of fine-grained metering electricity consumption data: Comfort-enabled energy management services, which will trade off individual or aggregated building-scale comfort preferences, either learned either explicitly set up by energy consumer from consumers against DSO/energy flexibility needs; Energy consumer optimised consumption profiling to be deployed to ESCO and retailers and DSO, aimed to either deliver ad-hoc situated energy offer which takes into due consideration the comfort (es. Heat) preferences of the resident or may allow retailers and DSOs (for different purpose) to better predict energy load flexibility. Here information on end user comfort, habits or eventual healthcare problems, may be of utmost interest for these services; Personal safety at home where we will leverage on AI for predicting detecting abnormal intrusion and abnormal consumption patterns for electric loads to be controlled and switched off; Water-energy coordinated management services, with a view to leverage on a number of smart water pumps, whose operation is controlled in automated way by the local Water Network Operator (SII) as branch of ASM with a view to follow specific activation priority models, which may be coordinated with the electricity flexibility need, hence realising the synergy between electricity and water. In particular, from one side smart water pumps will be working as smart loads supporting the electricity network needs by leveraging on AI-based water demand forecasting. On the other way around AI will be leveraged to support selective operation of water network loads (i.e. hospitals).

ASM will directly use the project results to improve the asset management and the operational efficiency of the local smart distribution grid, as well to deploy cross-network coordinated management and operation with water networks. Yearly expenditures for operation and maintenance will also be optimised by advanced remote control of the asset.

Who will help implement this solution?

This pilot is implemented within the framework of the “I-NΕRGΥ: Artificial Intelligence for Next Generation Energy” Project. The I-NERGY Project has received funding from the European Union's Horizon 2020 Research and Innovation programme under grant agreement No. 101016508.

The responsible partner for this Use Case is ASM (https://www.asmterni.it/).

Learn more about I-NERGY pilots here: https://i-nergy.eu/pilots