t is low (around 25,000 systems). In these systems, high energy saving potentials are often identified through the optimization of heating, cooling and electricity consumption. In principle, however, too little data is available to date on the operating status of air conditioning systems, as monitoring components are often not installed when the systems are set up. With regard to energy inspections of HVAC systems, older studies have already recommended an optimized control strategy for approx. 50% of all systems and an expansion of sensor technology for approx. 40% of systems in order to leverage savings potential. The main problem is therefore that for most systems it is unclear whether the energy consumption during operation is necessary to achieve the desired goals, such as a certain ventilation capacity. This is where the project comes in and aims to use a mobile and stationary monitoring concept to analyze weak points in HVAC systems, identify potential savings, determine derived key figures and present them in a user-friendly way. These measures are intended to optimize operation and save energy, costs and CO2. Other goals associated with optimized system operation include increasing resource efficiency and extending the service life of the air conditioning systems. The application of the mobile monitoring system, which is used for a limited period of time and is based on a low material input, can already have a high positive environmental impact. The collected data is processed and evaluated in Excel checklists, for example, so that system operators can make informed decisions at an early stage (e.g. adapting operation, replacing components/systems, etc.). For the more comprehensive stationary monitoring system, the project relies on dashboards as a strategic tool for communicating information. Infographics on operating states and the processing of key figures are intended to enable system operators, building control technicians and energy managers to adapt system operation independently and continuously to save energy. Operation is optimized, for example, by adjusting the operating times, the volume flows of the ventilation and air conditioning systems and threshold parameters, which are essential for activating/deactivating certain operating modes. Specifically, four different properties were examined as demonstration buildings in the project: two small industrial companies, a hospital and a large office building complex. Accordingly, the technology installed is also very different and varies in complexity. The communication and division of tasks between the people involved in the properties is also very heterogeneous: The larger properties often lack contact with experts: While contact persons with comprehensive competencies and tasks are easier to reach at smaller properties and the systems can also be accessed if necessary, this often proves difficult at larger properties.

e extensive optimization measures were carried out based on monitoring data, for example to prevent simultaneous heating and cooling.

Two monitoring variants were used for the technical implementation. Mobile monitoring was used to record and evaluate the status quo, while stationary monitoring with permanently installed components provided additional insights into operation and enabled continuous operational optimization. Mobile monitoring requires fewer components. The resource-energy requirement of the technology for mobile monitoring is estimated to be very low in the project. It amounts to a few additional sensors and meters. Nevertheless, mobile monitoring can already achieve enormous savings, particularly with regard to the functionality and dimensioning of the systems and their operating parameters.

The implementation of stationary monitoring required additional hardware and more data. In principle, the type and number of additional components required depends heavily on the size and basic equipment of the property and the systems in place. However, by recording detailed operating states, more in-depth analyses are possible, which can then be used to tap into further savings potential.

y evaluated using extrapolations according to general physical rules, with weighted adjustments made for weather influences. Based on this, the CO2 savings were calculated using the emission factors for energy audits published by the Federal Office of Economics and Export Control.¹

The optimization results achieved in accordance with the DIN EN 15232 and ISO 52120 standards were remarkable, with average electricity savings of > 30 % across the board. The basic adjustment of the volume flow (target/actual adjustment, adjustment of operating times, etc.) in particular is an easy measure to implement, which can be carried out using mobile monitoring - i.e. with minimal use of measurement technology and resources. For some systems, the savings achieved in this way can be significantly higher than the average 30 %. This is because the power consumption is halved if the volume flow is reduced by just 20 %. For example, the volume flow for ventilating the basement area of a sample property could be reduced by 39 %, resulting in a 78 % reduction in electricity consumption. This means that around 270 MWh or 198 tCO2eq can be saved each year. Further efficiency potential can then be tapped by fine-tuning the systems depending on room occupancy/room use. However, this requires stationary monitoring, as longer periods of time must be considered. Another positive environmental effect that goes hand in hand with the optimization of operation: due to the low usage, the systems also wear out less. This can significantly increase the service life. The project network assumes possible increases in service life of up to 10 years. Resources are therefore used for longer and emissions are avoided. Fault detection is also simplified or accelerated, which can lead to faster and more effective maintenance and servicing of the systems. Negative environmental impacts of digital applications were not included in the balance. Depending on the existing system technology and building management systems, the emissions caused by monitoring and data processing vary in the individual properties. Using the example of the large office building complex (largest property examined): here, over a hundred additional sensors (air temperature, irradiance, humidity, presence detectors, CO2, pressure), around 50 meters (electricity, volume flow, heat quantities), several communication systems (BUS systems, routers, repeaters, laptops) and several hundred meters of cable (electricity, air, LAN) for stationary monitoring were also installed. In total, this property generates 10 GB of data per year. With such comparatively complex properties, there can also be other obstacles that can reduce the effectiveness of the measures. The building management system and its accessibility (closed interfaces, lack of expert level) should be mentioned here in particular, which can make it difficult to implement the recommendations for action to optimize operations. Predictive control strategies, which could potentially result in even greater savings potential, are therefore often costly to implement. At the same time, it makes sense to support the system operators in implementing the possible operating optimization strategies (e.g. manual adjustment of the volume flow or operating times), as the project showed that in some cases there is little expertise and previous points of contact in this regard. Despite the material costs and technical and organizational hurdles, electricity savings of > 30 % were achieved across the board, even in large properties.