Energy Informatics: A research blog

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...