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

Project description

Predicting the weather using artificial intelligence

The EU-funded ISOBAR project aims to exploit Artificial Intelligence and develop five main AI components to support the Demand Capacity Balancing (DCB) supply chain in non-nominal and critical situations: enhancing and anticipating the prediction of convection, better characterising capacity and demand, identifying weather-related imbalances between capacity and demand and selecting capacity-demand mitigation measures at local and network levels. The project will be built on four main pillars. First, it will reinforce collaborative Air Traffic Flow and Capacity Management (ATFM) processes at pre-tactical and tactical levels into the local and network roles integrating dynamic weather cells. Second, it will characterise the demand and capacity imbalances. Third, it will set up a user-driven mitigation plan. Ultimately, the project will develop an operational and technical roadmap for the integration of ancillary services into SWIM.

Objective

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:
a) Reinforce collaborative ATFCM processes at pre-tactical and tactical levels into the LTM (local) and Network Management (network) roles integrating dynamic weather cells.
b) 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.
c) 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.
d) 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.

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RIA - Research and Innovation action

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Call for proposal

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(opens in new window) H2020-SESAR-2019-2

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Coordinator

CENTRO DE REFERENCIA INVESTIGACION DESARROLLO E INNOVACION ATM, A.I.E.
Net EU contribution

Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.

€ 315 750,00
Address
CALLE CAMPEZO, 1, 4º, EDIFICIO 7, PARQUE EMPRESARIAL LAS MERCEDES
28022 Madrid
Spain

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Region
Comunidad de Madrid Comunidad de Madrid Madrid
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Research Organisations
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Total cost

The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.

€ 315 750,00

Participants (10)

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