Artificial Intelligence (AI) helps heat pumps to operate more efficiently by avoiding incorrect device settings and optimizing system operation.
The Fraunhofer Institute for Solar Energy Systems (ISE) is researching a new generation of smart heat pumps that use artificial neural networks to adapt to environmental conditions and learn as conditions change. This increases energy efficiency and user comfort. Extensive simulations showed promising potential energy savings of 5 to 13 per cent and increased comfort. Measurements in an initial field test in an actual building confirmed these results.
In the “AI4HP” project, Fraunhofer ISE, together with the company Stiebel Eltron and the French research partners CEA List (Laboratory for Integration of Systems and Technologies) and LPNC (Laboratoire de Psychologie et NeuroCognition) as well as the industrial partner EDF R&D, has gathered important findings on new adaptive control methods for heat pumps based on neural networks. They focused on the potential, flexibility and practical suitability of AI controls. Up to now, heat pumps for residential heating have mainly been controlled using static heating curves set once during installation. In most cases, the curves have not been optimized for the building, as this is only achievable through a time-consuming calibration. Furthermore, heating curves do not account for short or long-term changes, such as solar radiation, occupant usage or building renovation and aging. In this project, the specific building behaviour patterns, e.g., how it changes with varying solar radiation, are learned by artificial intelligence (AI), which continuously analyzes recorded measured values.
“AI methods must become more robust and scalable to implement them cost-effectively in a large number of different building types,” said Dr Lilli Frison, project manager at Fraunhofer ISE.
“Furthermore, only reliable and trustworthy methods that guarantee safe operation will be accepted by heat pump manufacturers and their customers,” colleague Simon Gölzhäuser said.
Artificial neural networks can map complex and highly non-linear relationships very accurately and, therefore, are suitable for this purpose. Thus, the research team developed a neural network based on time series prediction within the “AI4HP” project. The novel transformer architecture enabled the network to link historical and future input data and thus estimate the temporal course of the room temperature. The intelligent heat pump controller, developed in the project, uses an artificial neural network to digitally represent the building’s thermal behaviour and a real-time capable optimization algorithm to optimally regulate the flow temperature of the heat pump.
Field test confirms positive results
The new AI heat pump controller was evaluated in extensive simulation tests, in which three buildings, each of a different construction year and refurbishment status, were simulated for one heating season. The questions on self-calibration and adaptability to new environmental conditions were answered positively. Depending on the building, the resulting energy savings were shown to be 13 per cent on average compared to the standard heating curve. These savings were due, in particular, to an improved matching of the reference room temperature and the setpoint temperature. Further energy savings can be expected if the controller is extended to include the efficiency characteristics of the heat pump.
On top of this, an initial field test in an actual building confirmed the functionality of the new controller. The one-week test operation showed that both the achievement of the setpoint temperature (average deviation reduced by more than half) and the coefficient of performance (COP) improved significantly with the controller. Compared to the reference period, the AI controller recorded a COP increase of 25 per cent. However, this needs to be evaluated in more detail during more extended field test series and with different building types. Notable is that the algorithm established stable heating curve parameters after just a few days. Since these parameters are optimized for the specific building, they can be used to increase operation efficiency in systems with conventional heating curves. Despite this great potential, the experience from the field test also showed that a good controller performance requires a high accuracy in the AI building model.
The French project partners within the binational project consortium focused on optimizing the operation of hot water heat pumps. The intelligent algorithm for operation optimization was tested in a climate chamber as part of a hardware-in-the-loop laboratory test using an actual heat pump and an accurate consumption profile. The results suggest that AI prediction, combined with optimized heat pump control, can potentially reduce electricity consumption for hot water supply by up to 8 per cent.
