Empa's Self-Learning Algorithm to Revolutionize Energy Management
A groundbreaking self-learning algorithm, developed by Empa researchers, is set to revolutionize energy management in buildings and electromobility. This innovative solution, brought to market by the start-up 'Kuafu', promises significant reductions in CO2 emissions while maintaining user comfort.
The algorithm, designed to optimize energy consumption, takes into account local production, available storage, and network availability. It preferentially draws power from the grid when it's available from renewable sources, minimizing CO2 emissions during operation. This smart system can reduce a building's CO2 emissions by over 10 percent without compromising resident comfort.
To ensure scalability, the future of this technology relies on consistent digitization of buildings. The algorithm enables buildings to communicate proactively about their power needs and availability, particularly during peak times. This not only enhances network stability but also promotes consumer flexibility.
Empa researchers, through their start-up 'Kuafu', are committed to bringing this technology to the market. Their goal is to contribute to energy optimization and decarbonization in the building sector and electromobility. The 'Empa Entrepreneur Fellowship' recipient, whose name was not found in the provided search results, is leading this effort to make buildings more energy-efficient and sustainable.
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