Browsing by Department "Forschung Burgenland"

Due to the increase of volatile renewable energy resources, additional flexibility will be necessary in the electricity system in the future to ensure a technically and economically efficient network operation. Although home energy management systems hold potential for a supply of flexibility to the grid, private end users often neglect or even ignore recommendations regarding beneficial behavior. In this work, the social acceptance and requirements of a participatively developed home energy management system with focus on (i) system support optimization, (ii) self-consumption and self-sufficiency optimization, and (iii) additional comfort functions are determined. Subsequently, the socially-accepted flexibility potential of the home energy management system is estimated. Using methods of online household survey, cluster analysis, and energy-economic optimization, the socially-accepted techno-economic potential of households in a three-community cluster sample area is computed. Results show about a third of the participants accept the developed system. This yields a shiftable load of nearly 1.8 MW within the small sample area. Furthermore, the system yields the considerably larger monetary surplus on the supplier-side due to its focus on system support optimization. New electricity market opportunities are necessary to adequately reward a systemically useful load behavior of households.

Sustainable energy consumption behavior in households is a decisive factor in contributing to climate protection. To provide residents comprehensible insights into their household’s energy consumption data, nonintrusive load monitoring on appliance level can assist. To give consumers a simple and comprehensible overview of their consumption data, which they also find aesthetically pleasing, in this work a two-stage experimental design is used to compare the perceived aesthetic appearance of different types of electricity consumption graphs on appliance level (aggregated or time-dependent) and their triggered emotion. First, a representative survey covers the general aesthetics assessment in a 2x3 mixed factorial design. Second, an in-depth eye-tracking and facial expression investigation is used to track and quantify emotions on different electricity consumption graphs on application level and their perceived advantages and disadvantages. The main results provide comprehensive insights into the aesthetics of electricity consumption visualizations to trigger positive emotions, in particular users’ preferences for simple and conventional graphs like bar charts.

In Industry 4.0, Cyber-Physical Systems (CPS) are formed by components, which are interconnected with each other over the Internet of Things (IoT). The resulting capabilities of sensing and affecting the physical world offer a vast range of opportunities, yet, at the same time pose new security challenges. To address these challenges there are various IoT Frameworks, which offer solutions for managing and controlling IoT-components and their interactions. In this regard, providing security for an interaction usually requires performing additional security-related tasks (e.g. authorisation, encryption, etc.) to prevent possible security risks. Research currently focuses more on designing and developing these frameworks and does not satisfactorily provide methodologies for evaluating the resulting costs of providing security. In this paper we propose an initial approach for measuring the resulting costs of providing security for interacting IoT-components by using a Security Cost Modell ing Framework. Furthermore, we describe the necessary building blocks of the framework and provide an experimental design showing how it could be used to measure security costs at runtime.

Many online services and their service providers require the electronic proof of identity for the secure authentication of citizens. Internet of Things (IoT)- and Cyber-Physical Systems (CPS)-services and devices are increasing and these are used in different areas. Furthermore, the increasing distribution of online services and IoT devices need to be monitored, especially in critical infrastructure. The proposal of a framework to authenticate and identify people, devices and services can be a useful tool to improve security and trust in CPS, linking them with identified people by utilizing and combining tools which do exist in isolation. IoT frameworks and identity protocols combined with responsible people, hardware, smartphone-applications, and certification authorities can provide secure authentication, trustworthy communication and the management of identities and permissions. This position paper proposes an IoT-framework for (critical infrastructure) service providers and public administration to authenticate, identify and manage their running devices and services as well as people, their electronic identification and sent records. This can improve the reaction time and processes additionally providing trust and secure communication between people, devices and services, especially for authorities in critical infrastructure areas, where humans and their safety are particularly important.

Facing food insecurity and overuse of resources due to effects of climate change, humanity needs to find new ways to secure food production and produce close to consumers. Vertical farming, where plants are grown in vertical arrays inside buildings with help of Information and Communication Technology (ICT) components, could contribute to solving this issue. Such systems integrate heterogeneous devices on different computing layers and acquire a lot of data to monitor and optimize the production process. We created an indoor testing unit in which growing conditions can be monitored and controlled to optimize growth of microgreens. This setup includes an Indoor Farming Support as a Service (IFSaaS) prototype that provides safe and secure monitoring and controlling, as well as self-adaption of an indoor farming system. In this article we provide information about the combination of most suitable technologies.

The global electric vehicle market is rapidly growing, which is an important step, as electro-mobility is an essential driver for achieving climate targets. However, acceptance of electric vehicles is still lacking among potential users. To overcome their skepticism, it is necessary to understand the decisive factors for the consumers’ decision-making process. Therefore, this work investigates essential drivers and barriers to electric vehicle adoption derived from international literature. Electric vehicle users from the electro-mobility-club Austria were interviewed by means of an online survey. Importance-performance analysis was conducted to analyze the data. Interpretations are supported by means of receiver operating characteristic analysis. The results show that the environmental performance of electric vehicles is a key motivator for participants and that this is already perceived as satisfying. Major barriers governments and manufactures should address are acquisition cost, range and time consumption associated with the usage of electric vehicles.

Energy consumption at the household level has a significant environmental impact. However, it is often difficult for end users to understand this complex topic. Hence, end users need to sight and understand their own energy data by means of appropriate visualizations to achieve a sustainable change towards more efficient energy consumption behavior. By means of an experimental online survey, the comprehension of energy data visualizations with regard to the daily electricity consumption of a multi-person household was surveyed. A total of n = 538 respondents were asked about their comprehension at three levels of difficulty, considering the response duration. The results show that comprehension differs depending on the energy data visualization type and its information density. This work contributes to determine appropriate visualization types to unleash potential for more energy efficient end user behavior.

This work investigates a novel distance-based electricity tariff applicable to energy communities. Based on a mixed integer linear optimization model computational studies are conducted to investigate the effects of this tariff on a small-scale community consisting of 22 consumers and 5 prosumers. Additionally, the potential of electric storage systems supporting the use of locally produced energy is investigated. The results of the computational model prove that an energy community generates incentives photovoltaic plants and the installation of energy storage systems. Complementary to the simulations, the flexibility options are supported by a user survey focusing on technology attributes for local energy market models, proving that there is a significant awareness for the use of energy storage systems.

Die EU 2020 Ziele zur Senkung des Primärenergieverbrauchs bzw. Steigerung der Energieeffizienz fordern auch die moderne Gebäudetechnik zum aktiven handeln auf. Dafür braucht es eine ganzheitliche Betrachtung aller Systeme und Anlagen im Gebäude. Es reicht nicht aus nur einzelne Teilsysteme, wie beispielsweise Wärmebereitstellung oder Wärmeabgabesystem, bilanziell zu bewerten. Die Senkung des Primärenergieverbrauchs durch Steigerung der Energieeffizienz setzt eine genaue Kenntnis der Energiestromverteilung in den hydraulischen Verteilnetzen voraus. In dieser Arbeit rückt die non-invasive Temperaturmessung in den Mittelpunkt. Anhand eines vereinfachten Widerstandsmodells wird ein Korrekturalgorithmus entwickelt, der imstande ist, die Messabweichung zwischen invasiven und non-invasiven Temperatursensoren zu korrigieren. Bei dieser Methodik reicht die messtechnische Erfassung von Rohroberflächentemperatur und Umgebungstemperatur aus, um die annähernd wahre Temperatur des im Rohr strömenden Mediums zu ermitteln. Der Einsatz einer thermischen Isolierung rund um die non-invasive Messstelle wird ebenfalls thematisiert.

Black box modeling is a fast and efficient way of creating models for generating the heat demand of a district heating networks. A sufficient amount of high quality data has to be collected to form the basis for a valid model that can serve as training and test stand for the models. The model parameters and their influence on the heat demand are investigated and a model structure is derived. With this structure, five data mining algorithms, namely Multiple Linear Regression (LR), Support Vector Regression (SVR), Random Forest (RF), k-Nearest Neighbor (k-NN) and Artificial Neural Networks (ANN) are utilized for creating the load models for a small district heating network located in southeast of Austria. Except for LR, all algorithms showed a good performance. They are well suited for that kind of task. K-NN has the best regression score metric with an average MAPE of 13.49 %.