Intermittent renewable sources cause imbalance and disruption that costs 20B€/year externalities only in the EU ultimately covered by final bill payers.
FlexyAI aims to develop an Artificial Intelligence (AI) machine learning solution, highly integrated with a Cyber Physical Systems (CPS)/Internet of Things (IoT) platform to introduce energy efficiency and optimize Demand Side Flexibility (DSF). The ‘smart’ energy demand response introduced by this solution will ultimately help the network rebalance.
Within the ‘Reach’ programme the Idea75 challenge “DATA DRIVEN TECHNOLOGY FOR EFFICIENCY IN ENERGY INTENSIVE INDUSTRIES” will be addressed to test the data science module of our solution – powered by a state-of-the-art Artificial Intelligence engine – and to process the CPS/IOT data from energy-intensive industrial plants.
The data science engine in this project is used for the following objectives:
- An industry, energy consumption AI load forecast tool based on historical active power: current, energy, and voltage.
- An industry AI load forecast tool based on historical active power: current, energy, and voltage.
- An energy price forecasting tool to forecast the prices in the next 24-hour window.
- A set of AI-based predictive models for commodity price forecasting implemented as Robo-Advisory for optimal consumption profile load, aimed at the use of the energy-intensive industry Energy Managers.
Usage of Standards for data interoperability:
At this moment the project does not comply with any particular data interoperability standard, but it follows general guidelines of DISC (Data Interoperability Standards Consortium) and European Interoperability Framework (EIF).