NRG4Cast

Energy Forecasting

NRG4Cast is developing real-time management, analytics and forecasting services for energy distribution networks in urban/rural communities. We are analysing information regarding network topology and devices, energy demand and consumption, environmental data and energy prices data

NRG4Cast is developing real-time management, analytics and forecasting services for energy distribution networks in urban/rural communities. We are analysing information regarding network topology and devices, energy demand and consumption, environmental data and energy prices data. The services that will be integrated in a software module pipeline providing prediction and the decision support system based on network monitoring, anomaly detection, route cause analysis, trend detection, planning and optimisation. These services will be using advanced knowledge technologies in particular machine learning, data and text mining, stream mining, link analysis, information extraction, knowledge formalisation and reasoning. The platform will be tested in the two orthogonal case studies energy efficiency in municipalities and energy efficiency in city districts.

The two case studies will be complemented with the additional energy networks operated by project partners; electric vehicles network, public lighting system and energy positive buildings. The proposal concentrates on electric power networks through the development of a generic framework that will be able to control, manage, analyse and predict behaviour in an extensible manner on other energy networks like gas distribution, heat water distribution and alternative energy distribution networks. For these reasons a generic toolkit with programmable data adapters will also be developed. Proposal gathers highly competent RTD organisations, developers, energy operators and case studies from four European countries. The project is led by JSI, has a consortium of 8 partners from 4 different countries and will run for 36 months.

Projectinfos

Duration
01.12.201230.11.2015
Funding no.
600074