3 citations · 5 across the 4 of their papers we have counts for
5 papers
Exploring Bayesian Surprise to Prevent Overfitting and to Predict Model Performance in Non-Intrusive Load Monitoring
Richard Jones, Christoph Klemenjak, Stephen Makonin +1
Non-Intrusive Load Monitoring (NILM) is a field of research focused on segregating constituent electrical loads in a system based only on their aggregated signal. Significant compu…
Investigating the Performance Gap between Testing on Real and Denoised Aggregates in Non-Intrusive Load Monitoring
Christoph Klemenjak, Stephen Makonin, Wilfried Elmenreich
Prudent and meaningful performance evaluation of algorithms is essential for the progression of any research field. In the field of Non-Intrusive Load Monitoring (NILM), performanc…
Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring
Hafsa Bousbiat, Christoph Klemenjak, Gerhard Leitner +1
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 m…
Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation
Christoph Klemenjak, Stephen Makonin, Wilfried Elmenreich
Non-Intrusive Load Monitoring (NILM) comprises of a set of techniques that provide insights into the energy consumption of households and industrial facilities. Latest contribution…
On Metrics to Assess the Transferability of Machine Learning Models in Non-Intrusive Load Monitoring
Christoph Klemenjak, Anthony Faustine, Stephen Makonin +1
To assess the performance of load disaggregation algorithms it is common practise to train a candidate algorithm on data from one or multiple households and subsequently apply cros…