Resilience and Automation in Energy Industry Processes

Challenges

• Trouble-free, maintenance-free or low-maintenance operation without high costs or long downtimes for energy systems of all sizes – from domestic use to energy suppliers and distribution network operators
• A wide range of failure scenarios, from hardware and software failures to cyberattacks

Our solution and research work

• A resilient, self-learning and automated energy management system based on modern software architecture and the use of a wide range of AI methods, including those from the fields of big data, reinforcement learning and transfer learning 
• Use of resilient methods to prevent downtime 
• The use of distributed, asynchronous communication methods to prevent information loss and malfunctions in energy management 
• The use of self-learning and self-adaptive methods and process chains to enable automatic and autonomous adaptation to changing situations, whilst ensuring verifiability through Explainable AI and the ability to manually intervene in the processes and process chains at any time 
• Use of state-of-the-art encryption methods for the EMS methods, as well as for communication between the methods and between EMS clients