January 4, 2025, 4:34 pm | Read time: 3 minutes
Artificial intelligence can ensure the efficient operation of heat pumps. This is what the Fraunhofer Institute for Solar Energy Systems (ISE) found in a recent study. Initial tests show that the savings potential is up to 13 percent. How exactly does this technology work?
Heat pumps are synonymous with environmentally friendly heating, but there is still room for improvement in terms of efficiency. The Fraunhofer Institute for Solar Energy Systems has, therefore, been researching a new method. Artificial intelligence (AI) automatically adapts heat pumps to environmental and building conditions. With the help of neural networks, long-term savings and greater comfort can be achieved. Initial tests in buildings and laboratories are delivering astonishing results. Read more below on how to make heat pumps more efficient with AI
Heat Pumps Can Increase Efficiency and Comfort with AI
The project, funded by the German Federal Ministry of Education and Research (BMBF ), shows promising approaches for optimizing heat pumps using AI. According to the project, heat pumps can be significantly improved through the use of AI. The researchers have developed a system that uses artificial neural networks to increase energy efficiency and improve comfort. The study was published by ISE in December 2024.
The intelligent control system learns from continuously recorded measured values and dynamically adapts the heat pump to changing conditions. Simulations show potential savings of 5 to 13 percent, and initial field tests in real buildings have confirmed this.
Innovative Control Through Neural Networks
As part of the “AI4HP” project, ISE, together with partners such as Stiebel Eltron and EDF R&D, investigated adaptive control methods for heat pumps. Traditional heating curves, which are defined during installation, are often not optimally adapted to individual buildings. In addition, they do not take dynamic factors such as solar radiation or usage habits into account. The new AI-based control system uses neural networks to analyze and adapt the specific behavior of a building. Neural networks generally originate from the field of machine learning. The models make decisions in a similar way to a human brain.
“AI methods must become more robust and scalable in order to be implemented cost-effectively in a large number of buildings,” emphasizes Dr. Lilli Frison, project manager at Fraunhofer ISE. Her colleague Simon Gölzhäuser adds: “In addition, only reliable and trustworthy methods that guarantee safe operation will be accepted by heat pump manufacturers and their customers.”
Novel “Transformer Architecture” As the Basis
The Fraunhofer team developed a neural network based on the so-called “transformer architecture,” which links existing and predicted data. This structure makes it possible to forecast the room temperature precisely and optimize the flow temperature of the heat pump in real time.
Simulations with three buildings from different years of construction showed significant energy savings of 13 percent on average compared to conventional heating curves. Efficiency can be increased by precisely adjusting the room temperature to the set values.

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This Is Shown by Field Tests in Real Buildings
An initial field test confirmed the functionality of the new control system. Within a week, the deviation between the setpoint and actual temperature was reduced by more than half, while the COP value (“coefficient of performance”) increased by 25 percent. This value shows the ratio of energy required and heat generated under standard conditions.
The algorithm also quickly led to stable heating curves, which enabled an increase in efficiency even in classic operation. At the same time, the test showed that performance depends heavily on the precision of the AI-building model.