Projects of Group Schweiger

The Autology project is an important milestone in advancing technology developments for climate-neutral cities and the digitisation of the building stock in an automated way. It thus makes the following contributions to the tender priorities 1 and 2, in particular sub-theme 2.1: - Data management: The collection, analysis/processing, utilisation and documentation of building data play an important role in all life cycle phases (new construction, especially existing buildings) for optimal building operation. Compared to new buildings, data and information management including the creation of a suitable database (e.g. meter types, localisation, etc.) for Energy Services is associated with a significantly higher effort for existing buildings. Existing buildings in particular can benefit from automatically created ontologies, since the necessary information can be automatically extracted from the existing building automation and used for various purposes. The developments are thus - Digitisation of existing buildings: the planned project approach does not require any costly retrofitting of infrastructure, replacement of systems, etc., and from the current point of view can be integrated into existing automation systems with minimal hardware expenditure. - Improving energy and resource efficiency: Initial studies have shown that intelligent energy services (predictive control, FDD, demand side management, etc.) can reduce energy consumption by 15-30 %. In order for these technologies to find their way into building automation, information (e.g. time series data) of the sensors/actuators including semantic information must be easily available. This requires automatically created ontologies.

Im Projekt DISTEL sollen die Stärken der etablierten, modell- und datenbasierten Methoden in einem gemeinsamen Konzept vereint und so eine „zuverlässige AI“ geschaffen werden. Diese soll es ermöglichen, prädiktive Regelungen auch in komplexen Energiesystemen auf Quartiersebene einzusetzen und so deren optimalen Betrieb zu erzielen. Das Projekt DISTEL zielt darauf ab, eine Next-Generation prädiktive Regelung zu entwickeln, die Energiesysteme auf Quartiersebene mit mehreren Speichern (Kurzzeit- und Langzeitspeicher) optimal regelt. Somit soll das Quartier zum Energy-Hub werden und daraus resultierende Flexibilitäten für angrenzende Energiesysteme zur Verfügung stellen können.

Today, energy systems include prosumers and energy communities, which have novel objectives. They can collect detailed information of their consumption and generation. ECom4Future is leveraging this information for a more efficient planning and operation. Using a human-centred, multidisciplinary approach we will provide additional insights on how technical, psychological as well as legal frameworks influence the public support and the willingness to participate more actively in future energy systems. Based on the analysis of collectible information at customer level, we will identify and use power profiles from various sources to support the planning operation of energy communities and prosumer, considering also psychological and legal environments. Using market- based optimisation algorithms we will offer tailor-made solutions for creating, structuring, and operating complex prosumer installations and energy communities. Using machine learning based fault detection and diagnostics the data will be leveraged to improve the availability and safety of the technical installation on a prosumer level. The effort will be demonstrated and validated within the five international ECom4Future trial sites and laboratories, which including a large-scale trial site with 140kWh of grid-connected battery storage capacity. With the observing participation of three energy communities, we strive for a continuous exchange of research insight and real-world experiences.

In the iKlimEt research project, simulation tools (optimization, machine learning and energy analytics methods) are being developed for integral energy system planning taking into account the consequences of climate change and extreme events. The project goal is, among other things, the further development of a sector-coupled energy system optimization model (electricity, gas/hydrogen, heating/cooling, transport, demand-side management), which determines optimal expansion and operating decisions for the energy infrastructure. The validation of the developed tools takes place in a case study in the energy system of the Styrian energy networks in the spirit of climate neutrality in 2040.

The building sector makes a significant contribution to the advance of climate change. 36% of all GHG emissions in the EU come from the buildings sector (European Commission (2020): Energy Efficiency in buildings, no date), with healthcare facilities in particular being comparatively energy intensive. On average across OECD countries, the healthcare system is the largest CO2 emitter among all service sectors. (Climate Fund (2019): First time survey: healthcare system's carbon footprint substantial, no date). Key research question: how it is possible to best match indoor environments to people while reducing building energy consumption? Technological advances (e.g., IoT technologies) now allow easy measurement of indoor climate (temperature, humidity, CO2 levels, etc.). However, the main influencing factor on energy consumption remains actual building use. Accurate presence detection of people (occupancy) in the building to control heating and cooling demand is only expensive and complex to implement. In parallel, the necessary vital sign monitoring (heart rate, respiratory rate, heart rate variability (HRV)) of patients, residents of nursing homes, etc. via electrocardiogram systems (ECG) or blood pressure monitors is location-bound, cost-intensive and, due to the current setting, cannot be integrated into the building technology to ensure a comfortable indoor climate. Key Message: Research into new technologies (radar sensor for contactless vital sign monitoring), combined with IoT measurement of control variables relevant to building services (room temperature, humidity, CO2 content, occupancy) and application of artificial intelligence for data analysis creates technically new possibilities for the efficient and health-promoting operation of hospitals, nursing homes or retirement homes. Intelligent health monitoring based on this allows empirical, individualized information transfer for health-promoting, climate-friendly behavior of vulnerable groups and personnel.

At present, the building stock in the EU remains energy intensive and mostly inefficient; it is responsible for 40% of final energy consumption and 36% of CO2 emissions. In order to increase the share of renewable energy and reduce energy consumption, future systems must have a high degree of flexibility and efficiency. On the one hand, this requires the systematic embedding of cyber technologies in order to monitor the physical systems and enable communication between different subsystems. On the other hand, innovative energy services such as demand-side management or model-predictive control are required to reduce the energy consumption of buildings and to transform buildings into active, intelligent players in higher-level energy systems. Studies have shown that artificial intelligence (AI - Artificial Intelligence) is the backbone and enabler of many energy services. Key Message 1: Applications of artificial intelligence are the backbone and enabler of many energy services. Innovative Energy Services are built on a bi-directional, real-time interaction with real buildings. Innovative solutions are required for the generation, provision and evaluation of these large amounts of data. Internet of Things (IoT) technologies are the backbone and an enabler of these intelligent systems. Computing paradigms Current IoT implementation depends almost entirely on cloud infrastructure and cloud-based services. Cloud computing offers numerous advantages such as cost efficiency, high availability, inexpensive software and enhanced security [6]. However, cloud-based services also have serious disadvantages: reliability, trustworthiness, or security and data protection. These disadvantages result, among other things, from the fact that the data provider (end user) and the data consumer (cloud provider) often have conflicting interests. Key Message 2: In the area of ​​energy services for intelligent buildings, cloud computing has serious weaknesses in the areas of reliability, trustworthiness, and data protection/security. Edge computing is an alternative IoT implementation and refers to computing taking place at the edge of networks; the "edge" is where end devices access the rest of the network. Edge computing increases availability, accessibility and reliability and improves latency for many services compared to cloud computing applications. Users usually own the end devices and have physical access on site to control them. This also increases user trust as data migration becomes optional for many use cases, which in turn leads to a lower risk of data breaches. Key Message 3: In principle, edge computing can overcome the main problems and limitations of cloud computing. Edge devices have power consumption limitations and therefore have limited computing resources. Cloud-based AI applications usually consume a lot of energy and cannot (or only to a very limited extent) be used on resource-constrained devices. A central challenge for edge applications in the field of intelligent buildings is to "bring AI to the edge". Key Message 4: In order to use the full potential of edge computing for intelligent buildings, "AI must be brought to the edge" of networks.

The energy-intensive and inefficient building sector is responsible for around 40% of final energy consumption and 36% of CO2 emissions and thus plays a key role in achieving climate targets. To increase the share of renewable energy and reduce energy consumption, buildings, neighbourhoods, etc. must have a high degree of flexibility and efficiency. This requires the systematic embedding of cyber technologies (monitoring, communication between subsystems) and innovative energy services such as demand-side management or predictive control. Internet of Things (IoT) technologies in combination with Artificial Intelligence (AI) are essential for the generation, provision and evaluation of these large amounts of data.

The inspection of PV systems with infrared cameras for hot spot detection is state of the art, However, it is influenced by environmental parameters, which can lead to misinterpretations. the Electroluminescence is an innovative and much more meaningful method, which it through Energization allows defects (cracks, fractures, delaminations) to be found down to the cell level detect. A system is developed that is based on the electroluminescence measurement in Combination with domain-informed AI algorithms reliably monitor the state of modules diagnosed. This diagnostic method is developed in the laboratory and based on real PV systems evaluated. Only defective PV modules should be recycled in the circular economy supplied, degraded are to a second phase of use in the context of social projects be made accessible.

So-called "anergy networks" are based on a system approach that can contribute significantly to solving the major challenges of the energy transition. In the project, the knowledge on the topic is processed based on existing scientific work and realized projects and expanded in a targeted manner with the help of detailed analyses of an implemented case study.