Artificial Intelligence Solutions to Meteo-Based DCB Imbalances for Network Operations Planning

ISOBAR aims at the provision of a service- and AI-based Network Operations Plan, by integrating enhanced convective weather forecasts for predicting imbalances between capacity and demand and exploiting AI to select mitigation measures at local and network level in a collaborative ATFCM operations paradigm. To achieve this vision, four objectives are set:

• Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells.
• Characterisation of demand and capacity imbalances at pre-tactical level [-1D, -30min] depending on the input of probabilistic weather cells by using applied AI methods and ATM and weather data integration.
• User-driven mitigation plan considering AUs priorities (and fluctuations in demand based on weather forecasts) and predicted effectiveness of ATFCM regulations, considering flow constraints and network effects.
• Develop an operational and technical roadmap for the integration of ancillary services (providing AI-based hotspot detection and adaptative mitigation measures) into the NM platform, by defining interfaces, functional and performance requirements.