EnerMan envisions the factory as a living organism that can manage its energy consumption in an autonomous way. It will create an energy sustainability management framework collecting data from the factory and holistically process them to create dedicated energy sustainability metrics. These values will be used to predict energy trends using industrial processes, equipment, and energy cost models.
EnerMan will deliver an autonomous, intelligent decision support engine that will evaluate the predicted trends and access if they match predefined energy consumption sustainability KPIs. If the KPIs are not met, EnerMan will suggest and implement changes in energy affected production lines control processes: an energy aware flexible control loop on various factory processes will be deployed.
The EnerMan administrators will be able to use the above mechanisms in order to identify how future changes in the production lines can impact energy sustainability using the EnerMan prediction engine (based on digital twins) to visualize possible sustainability results when in-factory changes are made in equipment, production line.
The EnerMan digital twin will predict the economic cost of the consumed energy based on the collected and predicted Energy Peak load tariff, Renewable Energy System self-production, the variations in demand response, possible virtual generation, and prosumer aggregation.
Finally, EnerMan considers the operators actions within the production chain as part of a factory’s energy fingerprint since their activity within the factory impacts the various production lines. In EnerMan, we include a training mechanism with suggested personnel good practices for energy sustainability improvement through the production lines. Current and predicted energy consumption/sustainability trends on specific assets of the factory are collected and visualized in a virtual, extended reality model of the factory to enhance the situational energy awareness of the factory personnel.
This project received funding from the European Union’s Horizon 2020 Research and Innovation program under Grant Agreement n° 958478