Projects of Group Schweiger

The Austrian government is committed to accelerating the transition of its energy system and achieving CO2 neutrality by 2040. To achieve this goal, Austria must significantly step up its efforts to decarbonise all parts of its energy sector. Buildings account for about a third of the total energy demand. The government plans to phase out oil and coal heating systems by 2035 and limit the use of natural gas for heating in new buildings from 2025. Energy services such as predictive maintenance, demand-side management and model predictive control are central components for reducing the energy consumption of build-ings and transforming buildings into active, intelligent actors in higher-level energy sys-tems. The aim of BEYOND is to develop the technological foundation for “Next Generation En-ergy Services”, which is made possible by the interplay of the following technolo-gies: Virtual Reality for visualization and real-time interaction with the real building; Machine learning and physical simulation to show the real-life effects of interven-tions and decisions. IoT platforms for bidirectional real-time communication be-tween the building and its users. The technological developments are tested and evaluated on the basis of two use cases “Predictive Maintenance and Error Diagnosis” and “Human Aspects in Buildings”. Innovative companies in the field of energy services, building automation, simulation software and VR technology will benefit from the developments in BEYOND. Political decision-makers and end users will also benefit from the new possibilities of actively engaging with these next generation Energy Services.

The aim is to develop an IoT platform for the smart city. The first implementation takes place in the VS Graz - Leopoldinum. This IoT platform is the prerequisite for developments in the projects ANSERS and NextGES; in these projects (a) the user: interior comfort are collected, (b) through (machine learning-based) data analysis of heating, cooling & ventilation can be quantified, whether this can be increased, and (c) the energy consumption (electrical and thermal) can be analyzed and optimized.

Industrial energy systems for manufacturing are mainly designed for single supply technologies, not designed for the fluctuation of energy demand and energy supply and thus can only react to a volatile demand and supply (thermal and electric) to a limited extent. From this, the need for the best possible support in optimizing the operation of the industrial energy system (demand and supply), the interaction of different renewable (volatile) and conventional energy sources and the design for industrial energy systems can be derived. The demand for products from the printed circuit board industry is continuously on the rise. Besides the increase of companies in Austria have to face the challenge of frequent change and adaptation to end-user requirements, causing significant changes in the energy demand and supply and by this, energy capacity limits onsite. This will be further increased by digitalisation and in terms of site security the need to increase productivity. The flexibility of the system makes it almost impossible for industry to plan and assess necessary adaptations and investment in the process and supply system and these challenges will increase significantly in the upcoming years. The overall objective of DigitalEnergyTwin is to support the industry with the development of a methodology and software tool to optimize the operation and design of industrial energy systems. The core of the project is the development of a holistic optimization approach, based on (near-) real production data, historical and predictions of the existing system, both the process demand and supply level. By this, industry will be supported for the first time with reliable solutions in terms of fluctuating, volatile and renewable energy supply well designed for efficient process technologies. The methodology of the digital twin will be developed and validated for single use cases and more importantly implemented in the manufacturing industry (PCB industry). For selected processes (energy relevant) and renewable as well as conventional supply technologies also the product quality will be addressed within this approach. Simplification and the development of technical standard solutions will lead to costeffective exploitation in other industrial sectors. Thus, the DigitalEnergyTwin builds on other areas of digitisation that are currently being developed. The use of the digital twin methodology will also make it possible to use the augmented and virtual reality (AR/VR) approach, which enables efficient production and system monitoring as well training and will support the EnergyManager4.0 in the future. By this, a maximum impact and multiplication in other industrial companies and sectors will be achieved and the industry benefits from a reduction of costs and risk of investment decision, which will lead to a significant increase of the implementation of renewable energy technologies as well as technologies for higher energy efficiency in industrial production.

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.

SMART2B aims to 1) upgrade smartness levels of existing buildings through coordinated control of legacy equipment and smart appliances, 2) implement interoperability in two existing cloud-based platforms that are currently available in the European market and, as a result of this project, will be integrated into a single building management platform, 3) create a user-centric ecosystem that empowers citizens by simplifying equipment and device control and providing information about overall energy performance. The cloud-based platform will facilitate smartness upgrades of existing buildings, enabling their transition from passive buildings to active elements of the energy system by offering new energy and non-energy services such as increased energy efficiency, improved indoor comfort to the occupants and flexibility to various stakeholders including DSOs, building managers and other third-parties. Thereupon, specifically tailored to the needs of the user, SMART2B will provide new business models for the building energy market combining the savings from energy efficiency measures and gains from the active contribution of the building through flexibility services by exploiting the maximum level of smartness. The experience and maturity of solutions from the consortium partners will ensure market uptake through sound exploitation and replication activities carried out by the strong commercial backbone of SMART2B. SMART2B will develop and deploy non-intrusive IoT sensors and actuators in existing buildings aiming to solve one of the main problems of improving buildings’ indoor comfort and energy efficiency: the structural (physical and financial) limits of installing, monitoring, automating and control existing devices in buildings, by proposing plug & play devices able to interact with the appliances and legacy equipment already installed and communicate the collected data to the cloud for remote monitoring, data analysis based on AI and machine learning and control.

The goal of I-GReta is to develop solutions for planning and operation of highly flexible energy systems benefitting from storage capacities. I-GReta will connect 5 trial sites in 4 countries via a professional ICT platform benefitting from FIWARE components and thereby build a real-world digitalized and decentralized energy system. In recent years, studies and hands-on experience have concluded that the integration and participation of end-users are crucial for the energy transition. Occupants, owners and system operators as key need owners will participate and evaluate the operation of the respective systems in a Virtual Smart Grid which is based on the developed platform. A key use case will be the trading of storage capacities via the platform. The Austrian partners will contribute methodological expertise in the field of psychological and technical aspects of active user participation in smart energy systems with high shares of storages of different energy carriers. Furthermore, they provide mathematical and computational methods to enable large-scale modelling, simulation and optimization of cyber-physical systems. Prototypical developments will be tested and evaluated under laboratory conditions in the Austrian pilots.

The construction sector accounts for around 40% of the total final energy consumption and has enormous potential for saving energy and reducing CO2 emissions at low cost. The latest developments in digitization and the establishment of cyber-physical systems have the potential to significantly reduce the costs of building operations. Intelligent systems could access large amounts of data in order to reduce the energy consumption of buildings through optimized regulations that are adapted to actual needs. So far, this potential has only rarely been exploited. The overarching goal of the annex is to improve access to low-cost, high-quality data from buildings and to support the development of data-driven energy efficiency applications and analyzes. This enables the building controls to be optimized in real time and provides energy efficiency data and decision-making aids for building management. In order to achieve the overarching goal of the annex, the annex contains a number of specific goals on the subjects of (i) data acquisition, (ii) digital characterization of buildings, (iii) coding of knowledge in intelligent energy efficiency applications and (iv) support the use of data-driven products and services. These additional goals are 1) Providing knowledge, standards, protocols and procedures for the cost-effective collection and sharing of high quality data in buildings 2) Developing a methodology for creating control-oriented building models that enable testing, development and evaluation of the effects of alternative energy efficient strategies for HVAC - Facilitate control of buildings in a digital environment 3) Develop building energy efficiency (and related) software applications that can be used to reduce energy consumption in buildings and ideally commercialized. 4)Promotion of the transfer of the annex results through case studies, business model innovation and dissemination of results. The aim of Austrian participation in Annex 81 is to network with international project partners in the field of IT and building technology to identify global developments in this field, to actively participate in the cooperative technology development and thus to enhance Austria's competence (on the company side as well as on Side of the research). Through targeted dissemination activities and the involvement of relevant sectors and industries, this knowledge should also be shared with the relevant actors and stakeholders in Austria (control and smart home providers, real estate companies, building technology planners, facility management, IT sectors, architects, etc. ) to get redirected. Subsequently, the aim is to use the international networks gained from the annex, in particular to strengthen Austrian companies, such as permanent cooperation with annex partners in the area of ​​cooperative technology development, possible partnerships in the area of ​​Horizon Europe or at the level of classic business cooperation.

In the NextGES project, domain-informed machine learning methods and a user-centred approach are applied to develop energy services. The developed energy services will be tested in two Styrian demonstrators - "My Smart City Graz" and "Stanz" - improved and adapted to the needs of the local users. In the process, care will be taken to ensure that, through cooperation with the associated spin-off of Graz University of Technology, further development and research steps can be used. The creation of a roadmap necessary for this for the time after the project and for the further commercial utilisation of the project results will take place within the framework of the project.

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 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.

With the Renewable Expansion Act (EAG), the goal set by the federal government to cover 100% of total electricity consumption from 2030 on a national balance sheet from renewable energy sources is to be implemented. For this purpose, the annual electricity generation from renewable sources is to be increased by 27 TWh by 2030 through new construction, expansion and revitalization, with 11 TWh being realized by means of photovoltaic systems (PV). In 2019, the installed PV capacity in Austria was 1.7 GWp (1.7 TWh of generated energy). The annual growth rates in the years 2014-2018 were 150-180 MWp / a. Only in 2019 was it possible to achieve a higher value of 250 MWp / a. To achieve the target of 11 TWh by 2030, an annual expansion rate of 1,000 MWp / a would have to be achieved. Current studies show that the available roof areas with an expansion potential of 3.5 TWh are not sufficient and therefore alternative PV systems are required. For the reasons mentioned above, in addition to the planned roof systems, innovative alternative area potentials for PV systems must be tapped. The reports from other projects show the available space and energy potential, but these were determined using statistical methods, so that no statement is made regarding the actual suitability. Alternative PV areas / yields in a city were specified, but no methodically systematic localization of these places was carried out. The motivation of PV4EAG is to close this gap and to develop a process for the automated discovery of alternative PV areas. This addresses the sub-topic "Creation of a freely accessible data platform with energy-relevant forecasts" In the PV4EAG project, alternative PV areas are to be analyzed using methods from the field of Geographic Information Science & Technology and artificial intelligence for their suitability in terms of shading, yield potential, construction costs, and grid connection options. The starting point is the existing GIS data (digital building heights, vegetation models, terrain models from airborne laser scanning data, cadastral data), in combination with other publicly available data (Open Governmental Data) and other open data sources (OpenStreetMap, Corine Landcover, etc.), as well as remote sensing data and products (ie orthophotos and satellite image data) are used. By using smart data fusion, semantic annotation of the data (for semantic data integration), and spatial analysis techniques paired with artificial intelligence (GeoAI in the broadest sense), the data should be suitable for large-scale facade-integrated systems on high-rise buildings, sealed parking spaces in shopping centers and residential areas , Traffic areas and rail systems as well as floating PV are analyzed. For the development and testing of the analysis method, the project is limited to selected locations in Styria, with the aim of scaling it up. For these locations, a plausibility check is carried out using the existing VR installation in the EAS lab using a virtual 3D process, and detailed project planning is used to specify the realistic PV yield to be expected.

The project UserGRIDs develops two user-centred energy services at city district level, be¬ne¬fit-ting from active user participation and large amounts of real-time data. An ICT platform acts as a mid¬dleware providing seamless interoperability and standardized protocols. The first service is an energy management system (EMS) for districts with strongly fluctuating con¬sump¬tion and generation characteristics. The aim is to minimize emissions through optimal mana¬ge¬ment of en-ergy storage and supply from volatile sources. Various building controllers are ex¬ten¬ded to form a comprehensive, self-learning control system for the entire district. Its de¬vel¬opment is based on detailed thermo-electrical models also used by the second service, energy structure planning. It supports decisions on the transition of the district energy system towards zero greenhouse gas emissions. EMS, ICT-platform and energy structure planning will be im¬ple¬men¬ted, tested and further developed at the INNOVATION DISTRICT INFFELD of TU Graz.