EASE

Energy Infrastructure Facilities as Multi-Sensors for Forecasting and Diagnostics

Throughout the course of the project EASE, a comprehensive system will be studied by connecting historical and actual generation data with equipment data of geographically distributed energy infrastructure, which will establish a dense 'sensor' network.

With the help of this sensor network, precise and probabilistic short-term forecasts for power generation from renewable energy sources are to be developed on the basis of artificial intelligence, and with the help of the intersection of data from weather stations and weather forecasts. The connected view of the infrastructure will make it possible to perform a probabilistic path prediction of (extreme) weather events and in the case of such an event, to implement automated diagnostics to detect and classify damages and performance/power reductions. The core developments are going to be developed together with multiple companies in science and industry, to be organized in a shared dataspace, which allows enterprises in direct competition also to cooperate while safeguarding their own interests. The developed systems are tested on real plants within the framework of a proof-of-concept.

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