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There are 164 results.

Klimaneutrale Stadt

sustAIn4Build - AI competence for sustainable building management in climate neutral cities

The objective of the project sustAIn4Build is to increase energy efficiency and sustainability in the building sector by using artificial intelligence (AI). Industry-specific training programs support Austrian companies to develop a workflow for integrating AI technologies into their processes, enabling them to develop resource-saving, cost-effective and sustainable solutions. This strengthens their competitive­ness and contributes to the achievement of European decarbonisation goals.

Haus der Zukunft

thermocollect: realisation of a plus energy building by a solar active facade system

The project demonstrates the application of a solar active facade system in the course of the renovation of an existing building in order to achieve the plus-energy standard. Moreover, the effectiveness of the system will be demonstrated on new buildings and transparent compo­nents as well. The final outcome will be a manual for the application and implemen­tation of the façade system focusing on the segment of specialists and skilled labour.

Stadt der Zukunft

ÖKO-OPT-AKTIV - Optimised control and operating behaviour of thermally activated buildings in future urban districts

Development and simulation of scalable, distributed control strategies for the use of the storage effect of thermally activated components in buildings of future city districts for their energy supply by an energy centre.

Stadt der Zukunft

ÖKO-OPT-QUART - Economically optimized control and operating mode of complex energy networks of future city districts

In the project ÖKO-OPT-QUART energy-based, economic and control-orientated models will be developed in order to simulate the operating mode of complex, sustainable energy networks in city districts. For an exemplary configuration these models will be combined to an overall model which allows a realistic economic comparison of different control strategies. The final goal of the project is the development of a method for the systematic design of cost-optimized, predictive control strategies for complex energy networks in city districts.