HVAC systems are usually responsible for more than 40% of the energy consumption of residential and commercial buildings, making optimizing system efficiency a priority iboth economic and ecologically. It is expected that as the impacts of climate change increase, the need for HVAC systems will increase too. In particular, in commercial and industrial environment the need for direct cooling and ventilation will raise strongly. Comprehensive optimization of HVAC systems can typically reduce energy consumption and costs by 20 to 40%, improve system reliability through more efficient operation, and significantly reduce a building's carbon footprint. In opposite, in real life HVAC systems in existing buildings but also in new buildings are usually only parameterized during the planning and commissioning. Thereafter, ongoing monitoring, combined with a reconfiguration or optimization of the system, is usually neglected.

The aim of this project is to use machine learning, without explicit modeling, to evaluate and optimize system and operating states, as well as to plan predictive maintenance not only according to wear and associated costs but also in respect to their effects on building energy consumption. The focus of the optimization strategy is on the configuration of HVAC settings and the efficiency of maintenance planning. The added value of this approach is that there is no need to intervene in the control of the HVAC system. The developed AI can therefore also be used as a retrofit for existing systems across all manufacturers. In addition, the optimization can also take place on data from multiple systems and the learning phase of the AI ​​algorithms used is massively shortened through pre-trained models. The core of the technical innovation is an approach consisting of a combination of reinforcement learning, supervised learning and an iterative control strategy based on a Model Predictive Control (MPC) architectur, which copensates the deviations between the actual and expected values, e.g. minimization of model inaccuracies.

The evaluation of the system and the expected savings of 30% energy and 40% costs compared to existing systems is carried out in a dynamic system-in-the-loop simulation, which enables the developed technology to be tested in a wide range of use cases . Ultimately, however, the developed approach will be tested in a real system in a test building of the project partners over the long term in order to obtain experimental confirmation of the simulation results. The results and the proposed AI approach are of great relevance for building owners and facility managers, planners and system integrators, manufacturers and suppliers of HVAC systems and building management systems, building operators and tenants, all of whom are required to improve the energy efficiency of HVAC systems, e. g. due to the EU Directive 2012/27 / EU, significantly. Results will also open up new market segments for companies that provide and evaluate data.