Energy Informatics: A research blog

From 6th to 7th of November, the 4th European Workshop on Non-Intrusive Load Monitoring was held in London, United Kingdom. This special event brought together researchers and professionals to present and discuss latest developments in the area of NILM as well as its applications.  The sessions comprised topics such as commercial & industrial NILM, innovative algorithms, deep learning approaches, and evaluation. Also, several vendors such as  Voltaware ,  Qualisteo  or  Verv  introduced their latest products. In the poster & demo session, Christoph presented a poster on "Appliance Detection in Power Meter Readings". The poster illustrates how correlation can be utilised to detect electrical appliances in power readings. Especially for hardware with limited computational resources this approach shows promising results. For more information about the presented work refer to our paper on correlation filters for appliance detection. ...

We are delighted to announce that our paper " On the Applicability of Correlation Filters for Appliance Detection in Smart Meter Readings " was accepted and presented at this year's SmartGridComm conference in Dresden. With our load classification approach based on correlation filters, we aim to provide a low-cost classification method for measurement equipment with limited computational capabilities such as networked sensors or smart plugs. Abstract: "Communication systems utilise correlation filters to detect waveforms. In a broader sense, these filters examine the amount of resemblance between a template pattern and the input pattern. In the domain of smart grids, many applications require the detection of active electrical appliances, their condition as well as their current state of operation. Furthermore, the identification of power eaters, the recognition of ageing effects, and the forecast of required maintenance represent important challenges in (ho...

In the present day, the electric power grid faces an evolutionary step towards the smart grid. The smart grid is defined as the enhancement of the electric power grid with information and communication technology. This sort of digitisation will enable a bidirectional flow of energy and information within the power grid and provide several novel applications and allow to unlock the full potential of renewable energy technologies. To cope with the challenge of digitisation in power grids, key elements of future energy systems have to be explored and furthermore, computational methods have to be developed and refined. The Smart Grids group, located at the University of Klagenfurt, contributes to this challenge by investigating how power meter readings can be analysed to discover solutions to sustainably increase the energy efficiency of energy systems.  "We carried out a measurement campaign in eight selected households to track power consumption of individual electric...

On June 28th, the Workshop  „Mind, Culture and Behaviour in the Digital Age”  took place at the University of Klagenfurt. The emphasis of the workshop was on research perspectives and challenges in times of IoT, Big Data, Digital Humanities, and Digitisation. Researchers from the departments of technical sciences, economic sciences, and humanities were invited to deliver a speech. Christoph Klemenjak gave a talk "o n the importance of data in energy systems". State of the art, work in progress and open research questions were presented. Furthermore, the talk states the research findings of the Smart Grids research group. In particular, the talk presents the following contributions: YoMo Smart Metering Board -  yomo.sourceforge.net Mjölnir Energy Advisor Framework -  mjoelnir.sourceforge.net GREEND Energy Consumption Dataset -  greend.sourceforge.net Without any doubt data will be among the most important resources in future. Exactly as all other resou...

YaY - An Open-Hardware Energy Measurement System for Feedback and Appliance Detection based on the Arduino Platform "To analyse user behaviour and energy consumption data in contemporary and future households, we need to monitor electrical appliance features as well as ambient appliance features. For this purpose, a distributed measurement system is required, which measures the entire power consumption of the household, the power consumption of selected household appliances, and the effect of these appliances on their environment. In this paper we present a distributed measurement system that records and monitors electrical household appliances. Our low-cost measurement system integrates the YaY smart meter, a set of smart plugs, and several networked ambient sensors. In conjunction with energy advisor tools the presented measurement system provides an efficient low-cost alternative to commercial energy monitoring systems by surpassing them with machine learning technique...

Deep Neural Networks for Appliance Detection The objective of this thesis will be to explore the applicability of deep neural networks as appliance detectors. The student will be provided with training data, which includes the energy consumption of typical household appliances over the time span of one year. By using the data, the student will first label the data, train the DNNs and evaluate their performance. Depending on the thesis type, the student will have to compare the performance to another appliance detector. The selected approach will be implemented in Python or C++. Contact: Christoph.Klemenjak@aau.at Type: Scalable to all thesis types Supervised learning techniques for Energy Advisors Energy Advisors such as Mjölnir provide valuable feedback to the user. The feedback builds on gathered knowledge and observations of the energy consumption in households. The objective of this thesis will be: Review applicable supervised machine learning techniques and...