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Die Rolle der Energieeffizienz in modernen Rechenzentren
  • Computing-intensive processes – AI models, especially in the area of machine learning and deep learning, require immense computing power.
  • Exponential data requirements – AI systems are extremely data-hungry. Processing, storing and analyzing large amounts of data, from image and video data to text and sensor data, consumes a lot of power, especially in data centers.
  • Real-time and edge computing requirements – AI applications, such as autonomous driving, connected cities (smart cities) or the Internet of Things (IoT), rely on real-time data processing, which often takes place at multiple locations and at high speed. This requires additional energy-intensive systems, such as edge computing devices, to analyze data in real time.
  • Demand for ever larger models – As the race to develop ever more powerful AI systems continues, so do the demands on computing power. Advanced models such as ChatGPT or image generators (e.g. DALL-E) are growing in size and complexity, further increasing power requirements for training and deployment.
  • Artificial intelligence for green tech and energy management – AI is being successfully used to promote sustainable solutions and efficient energy use, for example through smart grids and energy optimization in industry.
  • Data center growth – To meet increased demand, companies are expanding their data centers or moving workloads to the cloud, where huge server farms are operated. These data centers not only require energy to operate the hardware, but also to cool the facilities.
  • ABB
  • Alfa Laval
  • Eaton
  • Linde PLC
  • Schneider Electric
  • Stantec
  • Trane Technologies
  • Quanta Services
  • Grid Connection
  • Thermal Technologies – heat recovery
  • Power monitoring, security solutions (cyber risk)
  • Back up power (hydrogen)
  • Hardware – modular Solutions
  • Data center design
  • Operations – HVAC management
  • Immersion cooling (Tauchkühlung)