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Research project (§ 26 & § 27)
Duration : 2023-03-01 - 2024-12-31

Scientific management of the research program "StartClim_2023" The climate research program StartClim was founded in 2002 by the climate research initiative AustroClim together with the Ministry of Environment. It is a flexible instrument that can quickly address current issues in the field of climate change due to its short duration and annual allocation of projects. Within the framework of StartClim projects, new topics related to climate or climate change can and should be researched in an interdisciplinary manner from a wide variety of perspectives and by a wide variety of disciplines. In the project "StartClim2023 - Scientific coordination" the scientific management for the quality assurance of the implementation of the StartClim program in the tender period 2023 is implemented.
Research project (§ 26 & § 27)
Duration : 2022-03-01 - 2023-12-31

Scientific management of the research program "StartClim_2022 The climate research program StartClim was founded in 2002 by the climate research initiative AustroClim together with the Ministry of Environment. It is a flexible instrument that can quickly address current issues in the field of climate change due to its short duration and annual allocation of projects. Within the framework of StartClim projects, new topics related to climate or climate change can and should be researched in an interdisciplinary manner from a wide variety of perspectives and by a wide variety of disciplines. In the project "StartClim2022- Scientific coordination" the scientific management for the quality assurance of the implementation of the StartClim program in the tender period 2022 is offered.
Research project (§ 26 & § 27)
Duration : 2023-11-01 - 2026-10-31

The climate crisis is leading to an increase in extreme and unfavorable weather events and is having a major impact on the reliable operation of the energy infrastructure, be it through temporary events such as dark calms and icing or through damage caused by hail and storms. Both lead to high costs, on the one hand if balancing energy has to be provided unexpectedly, and on the other hand because insurance companies have to compensate for the damage. The protection standards that apply to infrastructure built today may no longer be sufficient in future climate scenarios and increased damage and, in the worst case, failures could occur. If these cannot be averted, they should at least be foreseen as precisely as possible in order to avoid worse things or to take timely measures. However, current methods do not make it possible to accurately predict local and short-term (extreme) weather events. Furthermore, information about the path and the time of arrival would be helpful for prediction and advance warning. On the one hand, to balance energy networks, and on the other hand, to prevent damage or to be able to react quickly. As part of the EASE project, a comprehensive system is to be researched in which historical and current data from geographically distributed energy infrastructure systems (e.g. PV, heat pumps, wind and water), weather forecasts and weather stations are networked with one another. This creates a dense 'sensor' network, which enables locally specific and precise short-term forecasts and an improvement in automated system diagnostics based on artificial intelligence (AI). Using AI, the huge amounts of data generated can be processed and potential benefits can be derived for various stakeholders. As a result, small-scale and short-term forecasts and diagnostics of generation plants are possible. From what happens in neighboring systems, it is learned by machine how parameters will develop locally at a target location, e.g. B. Cloud trajectories. On the one hand, the energy production of volatile renewables can be predicted with high accuracy and at the same time the development and path of (extreme) weather events can be recognized locally

Supervised Theses and Dissertations