Showing posts from November, 2017

European Workshop on Non-Intrusive Load Monitoring (NILM)

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 VoltawareQualisteo 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.
Picture: Christoph Klemenjak presenting his…

Paper on "Correlation Filters for Appliance Detection" @ IEEE SmartGridComm 2017

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.

"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 (home) energ…