Spring Meeting of Smart Grids Austria at University of Klagenfurt: A Step Towards a Sustainable Energy Future

On May 14, 2024, the Spring Meeting of the Smart Grids Austria technology platform took place for the first time at the University of Klagenfurt. Established in 2008, this platform connects key players from the energy sector, industry, and research, serving as a vital resource for public authorities. Its mission is to foster research and development and implement innovative technologies for a future energy system that is both energy-efficient and cost-effective.

The meeting attracted 70 participants from across Austria, despite inclement weather, highlighting the platform’s importance in advancing Austria’s energy transition. The event featured a series of engaging presentations, beginning with Christoph Wanzenböck, the platform’s managing director, and other key figures from academia and industry.

Alfons Haber from E-Control kicked off the morning session by discussing the Electricity Industry Act and its adequacy for supporting the energy transition. Following a coffee break, the focus shifted to “Smart Grids in Digital Implementation,” with speakers presenting on topics such as digital twins in distribution networks and practical examples of digitalization in the energy sector.

The afternoon session addressed “Flexibilities in Implementation,” covering regulatory and technical aspects essential for integrating flexibility into the energy system. Presentations included the potential of photovoltaics and the role of traffic infrastructure in supporting the energy transition.

The event concluded with an interactive segment where students showcased their projects, fostering discussions on future collaborations and project ideas. Overall, the meeting provided valuable insights into the current developments and challenges facing smart grids and the energy transition in Austria, emphasizing the need for continued efforts to achieve a sustainable energy future.

Nature-Inspired Solutions for Energy Management

In the rapidly evolving field of energy management, innovative solutions are essential to meet the growing demands for efficiency and sustainability. Against this backdrop, Kristina Wogatai presented her dissertation, “Exploring the Potential of Self-Organizing Applications in Energy Networks,” at the Doctoral Symposium of the 4th IEEE International Conference on Autonomic Computing and Self-Organizing Systems (ACSOS 2023) in Toronto. Her research addresses the challenges of stable energy supply networks in the face of increasing demands for efficient and sustainable energy management.

Plasmodium of Fuligo septica (Photo by W. Marcher)

Focusing on self-organizing applications, Kristina’s work enables network components to communicate and adapt without centralized control, inspired by the efficient pathways of slime molds. These fascinating organisms, which can exist as single cells or aggregate into multicellular structures, are known for their remarkable ability to find the shortest paths to food sources and optimize their growth patterns. By studying both simulated and in-vitro slime molds, Kristina aims to harness their natural problem-solving capabilities to inform the design of adaptive energy networks.

By following this approach, Kristina aims to develop fault-tolerant architectures that ensure network stability through redundancy and self-healing mechanisms. By integrating nature-inspired approaches with advanced technologies like artificial intelligence, her research aims to enhance energy grid management and contribute valuable insights to the field of self-organizing applications in energy networks.

Stellenausschreibung Senior Scientist mit Doktorat (w/m)

Die Universität Klagenfurt schreibt folgende Stelle zur Besetzung aus:
Senior Scientist mit Doktorat (w/m) an der Fakultät für Interdisziplinäre Forschung und Fortbildung, Institut für Unterrichts und Schulentwicklung (IUS) im Beschäftigungsausmaß von 50 % (20 Wochenstunden, Uni-KV: B1 lit. b; http://www.aau.at/uni-kv). Das monatliche Mindestentgelt für diese Verwendung
beträgt € 1.944,80 brutto (14 x jährlich) und kann sich durch die Anrechnung
tätigkeitsspezifischer Vorerfahrung auf max. € 2.154,70 (lit. c) brutto erhöhen. Voraussichtlicher Beginn des bis 31.7.2023 befristeten Angestelltenverhältnisses ist der 1. Februar 2021.

Der Aufgabenbereich umfasst:

  • Mitwirkung im Forschungsbereich Bildung für nachhaltige Entwicklung und Weiterentwicklung des besonderen Studienbereiches Nachhaltigkeit an der Universität Klagenfurt
  • International orientierte Forschungstätigkeit (Publikationen, Akquisition und
    Durchführung von Drittmittelprojekten)
  • Selbständige Durchführung von Lehrveranstaltungen (inkl. Prüfungs- und Betreuungstätigkeiten)
  • Förderung des wissenschaftlichen Nachwuchses
  • Mitgestaltung der längerfristigen Weiterentwicklung und Förderung der interfakultären Vernetzung im Forschungsschwerpunkt Nachhaltigkeit und seiner Positionierung in der internationalen Scientific Community
  • Mitwirkung in der universitären Selbstverwaltung

Voraussetzungen:

  • Studienabschluss in Sozial-, Geistes-, Kultur- oder Wirtschaftswissenschaften und
    Doktorat mit deutlichem Bezug zum Thema Nachhaltigkeit. Forschungs- und Publikationsleistungen
    auf dem Gebiet Nachhaltigkeitsforschung
  • Einschlägige universitäre Lehrerfahrung und hochschuldidaktische Kompetenz,
    idealerweise im Bereich Bildung für nachhaltige Entwicklung
  • Ausgewiesene Kompetenzen im Bereich quantitativer oder/und qualitativer Forschungsmethoden
  • Kommunikations- und Teamfähigkeit

Der Nachweis für die Erfüllung aller Voraussetzungen für die Einstellung muss bis spätestens 25.11.2020 vorliegen.

Erwünscht sind:

  • Erfahrungen im Bereich der Nachhaltigkeitsforschung bezogen auf wirtschaftliche
    Zusammenhänge, Bildung und Kommunikation
  • Erfahrung in der Konzeption, Einwerbung und Leitung von Drittmittelprojekten
  • Erfahrungen in Bezug und Bereitschaft zu interdisziplinärer Kooperation
  • Gute Englischkenntnisse

Die Universität Klagenfurt legt im Rahmen ihrer Anstellungspolitik Wert auf Antidiskriminierung, Chancengleichheit und Diversität. Menschen mit Behinderungen oder chronischen Erkrankungen, die die geforderten Qualifikationskriterien erfüllen, werden ausdrücklich zur Bewerbung aufgefordert. Allgemeine Informationen finden BewerberInnen unter http://www.aau.at/jobs/information. Bei Interesse bewerben Sie sich mit den üblichen Unterlagen (Motivationsschreiben mit Angaben zu den Studienschwerpunkten, CV, Zeugniskopien, Darstellung der bisherigen Lehr- und Forschungstätigkeiten sowie mit einer charakteristischen Leseprobe (Artikel)) bis 25.11.2020.
Bewerbungen sind ausschließlich bei der Stelle mit der Kennung 661/20 in der Rubrik „Wissenschaftliches Universitätspersonal“ über den „Für diese Stelle bewerben“-Button
im Job-Portal unter jobs.aau.at möglich.
Es besteht kein Anspruch auf Abgeltung von Reise- und Aufenthaltskosten, die aus Anlass des Aufnahmeverfahrens entstehen.

Investigating Synthetic Data for Machine Learning Applications in Smart Homes

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Electrical consumption data contain a wealth of information, and their collection at scale is facilitated by the deployment of smart meters. Data collected this way is an aggregation of the power demands of all appliances within a building, hence inferences on the operation of individual devices cannot be drawn directly. By using methods to disaggregate data collected from a single measurement location, however, appliance-level detail can often be reconstructed. A major impediment to the improvement of such disaggregation algorithms lies in the way they are evaluated so far: Their performance is generally assessed using a small number of publicly available electricity consumption data sets recorded from actual buildings. As a result, algorithm parameters are often tuned to produce optimal results for the used datasets, but do not necessarily generalize to different input data well.

“We propose to break this tradition by presenting a toolchain to create synthetic benchmarking data sets for the evaluation of disaggregation performance in this work. Generated synthetic data with a configurable amount of concurrent appliance activity is subsequently used to comparatively evaluate eight existing disaggregation algorithms.“

Christoph Klemenjak

Instead of attempting to compile a benchmarking corpus from existing data sets, we present a methodological way to synthetically create data sets of definable disaggregation complexity. A high degree of realism can be accomplished by using accurate models of existing appliances and user activities. By forwarding synthetically generated data of gradually increasing levels of concurrent appliance activity to state-of-the-art disaggregation algorithms, we determine their sensitivity to specific data characteristics in a much more fine-grained way.

ANTgen – the AMBAL-based NILM Trace generator

We present a toolchain, ANTgen, that generates synthetic macroscopic load signatures for their use in conjunction with NILM (load disaggregation) tools. By default, it runs in scripted mode (i.e., with no graphical user interface) and processes an input configuration file into a set of CSV output files containing power consumption values and the timestamps of their occurrence, as well as a file summarizing the events that have occurred during the simulation). If you find this tool useful and use it (or parts of it), we ask you to cite the following work in your publications:

Andreas Reinhardt and Christoph Klemenjak. 2020. How does Load Disaggregation Performance Depend on Data Characteristics? Insights from a Benchmarking Study. In Proceedings of the Eleventh ACM International Conference on Future Energy Systems (e-Energy ’20). Association for Computing Machinery, New York, NY, USA, 167–177.

Learn more about the authors Andreas Reinhardt and Christoph Klemenjak

5th “Renewable Energies In Austria” report released

What do Austrians think about renewable energy technologies and related topics?

This question is examined annually by Prof. Nina Hampl and Dr. Robert Sposato in collaboration with Deloitte and Wien Energie in the Renewable Energies In Austria report series. At the core of this report lies a representative survey of over 1,000 participants conducted most recently in autumn 2019. Two clear signals emerged in this year’s report: a high level of acceptance for renewable energy technologies in general and broad support for climate policy measures.

As has been shown in the years before, the population holds a generally positive attitude towards renewable energy technologies: 77 % of the older respondents questioned are clearly in favour of building renewable energy technologies in their community. A number that is even higher among young respondents with 82 %. More specifically, photovoltaic power plants receive the broadest approval with 88%, followed by small hydropower with 74% and wind power with 67%.

An equally positive result was shown with respect to energy communities: Already around two thirds of the Austrian respondents are considering active participation in such communities, which allow private individuals to generate, consume, store and sell electricity or heat together. Austrian consumers attach particular importance to the fact that energy is generated locally and on the basis of renewable energy sources.

Albeit a little downslope from 2018 the group of potential electric car buyers also remains at a good level with 44 % considering to buy an electric vehicle as their next car. Again at 59 % young adults are even more interested in buying. Almost half of those interested in buying a car can imagine buying such a vehicle within the next five years.

Finally, with respect to the continuously dominant theme of climate change, the survey finds that there is a lot of support for planned policy measures regarding climate change mitigation. Two thirds of the respondents support the climate bonus for commuters who use public transport. A majority of 64 % would like an inexpensive 1-2-3 climate ticket for public transport, and 55 % consider CO 2 tariffs for non-climate-neutral imports to the EU to be sensible. Of particular interest to the Federal Government: an ecological tax reform with fewer taxes on work and fairer taxes on climate-damaging activities is conceivable for the majority surveyed respondents.

To find out more about the most recent report, download it here.

Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring

The global epidemic of the COVID-19 virus required severe restrictions on travel and meetings. Among many other events, also the International Instrumentation and Measurement Technology Conference (I2MTC 2020) could not take place physically.

Therefore, we made our paper presentation in the form of a video:

In her talk, Hafsa Bousbiat describes how abnormal behavior can be detected among common household devices using Non-Intrusive Load Monitoring. The need for reducing our energy consumption footprint and the increasing number of electric devices in today’s homes is calling for new solutions that allow users to efficiently manage their energy consumption. Real-time feedback at device level would be of significant benefit for this application. In addition, the aging population and their wish to be more autonomous have motivated the use of this same real-time data to indirectly monitor the household’s occupants for their safety.
By breaking down aggregate power consumption into appliance level consumption, Non-Intrusive Load Monitoring allows for reducing the energy consumption footprint and has the potential to indirectly monitor the elderly and help them to fulfil their wish to be more autonomous in a secure manner. Therefore, the work aims to depict an architecture supporting non-intrusive measurement with a smart electricity meter and the handling of these data using an open-source platform that allows us to visualize and process real-time data about the total consumed energy. The proposed architecture is depicted in the figure below.

Proposed architecture for integrating an AAL with an energy monitoring system

More details about our work can be found in the full version of our paper here.

Please reference the paper as follows:

Hafsa Bousbiat, Christoph Klemenjak, Gerhard Leitner, and Wilfried Elmenreich. Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring. International Instrumentation and Measurement Technology Conference. May 2020.

This work was supported by DECIDE – Doctoral school on “Decision-making in a digital environment” at the University of Klagenfurt.

Privacy vs. NILM: Obfuscating your Power Consumption with Load Hiding

With the development and introduction of smart metering, the energy information from costumers changes from infrequent manual meter readings to fine-grained energy consumption data. On the one hand, these measurements will lead to an improvement in costumers’ energy habits, but on the other hand, the fine-grained data produces information about a household and households’ inhabitants, which give rise to privacy issues because these monitoring results disclose user behavior which could be extracted by smart algorithms and techniques. The loss of privacy by load disaggregation and data mining is a huge upcoming smart grid and social issue which enforces the need for privacy-preserving techniques, which can be divided into the following three possibilities:

  1. Anonymization of metering data: The metering data and customer identity are separated by a third-party id
  2. Privacy-preserving metering data aggregation: Metering data is geographically encapsulated by aggregating the metering data of co-located consumers 
  3. Masking and obfuscation of metering data: Masking the power demand by adding or withdrawing the to the meter visible energy demand with the help of rechargeable batteries or controllable loads.
Load-based load hiding approach

In the paper

D. Egarter, C. Prokop, and W. Elmenreich. Load hiding of household’s power demand. In Proc. IEEE International Conference on Smart Grid Communications (SmartGridComm’14), Venice, Italy, 2014.

a state-of-the-art battery-based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented and compared. A load-based load hiding system controls appliances in a specific way to obfuscate a household’s power demand. For example, an electric water boiler could be instrumented to consume energy in a way that masks the power consumption of smaller household devices like coffee machines or a TV. There is no comfort loss expected for the customer: Overall, the boiler will consume a typical amount of energy and produce the expected amount of hot water.
Using this approach, however, reduces the predictability of your energy consumption, which is good for privacy, but a disadvantage for grid operators.

8 Open Positions at Alpen-Adria-Universität Klagenfurt

Alpen-Adria-Universität Klagenfurt (Austria) is establishing a Graduate School under the title ”Decision-making in a digital environment“ (DECIDE), and is therefore pleased to announce the following open positions:

8 Predoc Scientists (f/m)

These are 75 % employment positions (30 hours per week; Uni-KV: B1 – cf. www.aau.at/en/uni-kv), limited to 36 months. The minimum gross annual salary amounts to € 30.077,- and can increase depending on previous professional experience. Expected starting date is 1st October, 2019.

The graduate school “Decision-making in a digital environment” (DECIDE) is dedicated to interconnecting the ongoing digitalization and its effect on (human) decision-making. The distinctive feature of this graduate school is its interdisciplinary nature: The involved faculty members have a strong background in either social sciences and economics, computer science, engineering or psychology, perform research in their dedicated fields, and foster interdisciplinarity. The research projects to be carried out in the context of this graduate school address the current challenges of digitalization and decision-making from multiple perspectives, thereby exploiting the interdisciplinary setting of the graduate school. The faculty will encourage and strongly support successful candidates in their interdisciplinary research, in presenting their research at scientific conferences, as well as in publishing their work in international top journals and conference proceedings. The graduate school has a highly dynamic, familiar, and friendly attitude and thus provides a collaborative and very inspiring work environment with modern infrastructure.

Tasks and responsibilities:

  • independent research and scientific qualification within DECIDE with the goal to acquire a doctoral degree,
  • involvement in third-party funding proposals,
  • presentation of scientific results in publications and at conferences.

Required qualifications:

  • master or diploma degree in one of the following fields:
    • business administration,
    • cognitive science,
    • computer science,
    • economics (with a mathematical specialization),
    • information systems,
    • (technical) mathematics,
    • psychology,
    • electrical or computer engineering or
    • natural sciences (with a focus on economics),
  • good to very good study results with regard to the respective field,
  • good to very good written and spoken English skills.

All required qualifications are to be completed by 30th September, 2019

Desired qualifications:

  • good knowledge in one or more of the following areas:
    • simulation-based research methods (e.g., agent-based simulation, multi-agent simulation, Monte-Carlo simulation),
    • modelling of economic systems,
    • decision-support and recommendation systems,
    • decision-theory,
    • machine learning / artificial intelligence,
    • energy informatics,
    • networked autonomous systems,
    • diffusion processes,
    • decision-making and judgment,
    • management control systems,
    • quantitative research methods,
    • uni- and multivariate data analysis methods,
  • dedication to interdisciplinary research,
  • social and communication skills and ability to work independently,
  • basic experience in research projects,
  • subject-specific international, academic or practical experience.

The positions are aimed at the scientific training of graduates of a diploma or master’s programme with the goal of completing a doctoral study in the relevant field. Applications from persons already holding such a degree can therefore not be taken into account.

Alpen-Adria-Universität Klagenfurt seeks to increase the ratio of women in scientific positions and therefore encourages qualified female candidates to apply. Among equally qualified applicants, women will receive preferential consideration.

People with disabilities or chronic diseases, who fulfil the requirements, are particularly encouraged to apply. Applications from qualified candidates with a migration background are encouraged as well.

Applications should be submitted with the usual documents (application letter, CV, master/diploma thesis, certificates and further supporting documents) no later than 13 th March, 2019 mentioning the code 134/19 to Universität Klagenfurt, recruitment office. Applications can only be submitted online via www.aau.at/obf.

General information for applicants is available at www.aau.at/en/jobs/information.

More information on the graduate school on “Decision-making in a digital environment” is provided at www.aau.at/en/hda

Short-listed candidates will be invited to an interview. Travel and accommodation costs incurred during the application procedure cannot be reimbursed.

Call for Papers Energy Informatics 2019

Energy Informatics 2019 — Call for Papers
==========================================

The 8th DACH+ Conference on Energy Informatics
in Salzburg Austria from Sep 26, 2019 to Sep 27, 2019

Europe has put forward ambitious targets to reduce greenhouse gas emissions, to increase energy efficiency, and to raise the share of renewable energies. Energy Informatics is developing IT-based solutions necessary to achieve these targets.

The objective of the DACH+ conference series on Energy Informatics is to promote the research, development, and implementation of information and communication technologies in the energy domain and to foster the exchange between academia, industry, and service providers in the German-Austrian-Swiss region and its neighboring countries (DACH+).

We seek high-quality, original papers on smart energy systems and energy-efficient computing and communication.

Submissions describing theoretical contributions as well as system design, implementation, and experimentation are welcome. The list of topics of interest to the conference includes, but is not limited to:

* ICT for energy networks, micro-grids, and management of distributed generation.
* Energy-efficient mobility, charge management for electric vehicles, energy-aware traffic control, and smart grid integration of mobile storage.
* Smart buildings, digital metering, occupant comfort, and user interaction.
* Protocols and architectures for IT systems in the energy sector.
* Data analytics for smart energy systems and platforms for data analysis.
* Information systems for behavior change, market mechanisms, and business cases.
* Cross-cutting issues including: cyber security and privacy protection, interoperability, verification of networked smart grid systems, and more.

Submission and Publication
==========================

Submitted papers will be reviewed in a double-blind process.

Submission Deadline is on Apr 25, 2019

Notification Due Jun 10, 2019 and Final Version Due Jul 8, 2019

Accepted and presented papers will be published in the Springer Open Journal “Energy Informatics” https://energyinformatics.springeropen.com.

The Open Access fee for the journal article is included in the registration fee.

The conference language is English and papers must be written in English. We solicit full research papers (max. 18 pages of content plus 2 additional pages for references) as well as short papers (max. 10 pages of content plus 2 additional pages for references).

Submissions must be prepared following the Instructions for Authors, see https://www.energy-informatics.eu/.