Martina – Lakeside Labs

About
Research

News
Impressions
Alumni
Outreach
Research days
Startups
Directions
Jobs

Jessica Breitegger

Junior Researcher and PhD Student

Raphael Rauter

Founder and student

Henning Avenhaus

Senior Researcher

Siddhartha S. Borkotoky

Senior Researcher

Udo Schilcher

Senior Researcher

Florian Kandutsch

Management Assistant

Paul Ladinig

Junior Researcher and Founder

Evşen Yanmaz

Senior Researcher

Raphael Rauter

Katja Reiter

Florian Posch

Melanie Mayer

Paul Ladinig

Patrick Gregori

Project Assistant

Raheeb Muzaffar

Researcher

Selected Publications

Medium access with adaptive relay selection in cooperative wireless networks. IEEE Trans. Mobile Comput., 13(9), 2014.

An experimental study of selective cooperative relaying in industrial wireless sensor networks. IEEE Trans. Ind. Informat., 10(3), 2014.

Application-driven design of aerial communication networks. IEEE Commun. Mag., 52(5), 2014.

Guaranteeing global synchronization in networks with stochastic interactions. New J. Physics, 14:073031, 2012.

News

Robots inspect ship hulls
UWB4Industry: Self-sufficient UWB-based industrial sensor networks
Research days: Communication in swarms
Mini workshop on networked systems
Intelligent boarding to ski lifts
OptiMon: Reliable wireless monitoring for industry
Research days: Swarming in cyber physical systems
Visit and drone talk at CARRE Toronto
A year in the life of Lakeside Labs
Communication manager for disaster management

Newsfeed by Wilfried Elmenreich

Investigating the impact of data quality on the energy yield forecast using data mining techniques January 5, 2021
In this paper, we analysed the impact of using optimum combination of input variables and low dimensional subspace on Photovoltaic (PV) production forecasting accuracy. We worked in collaboration with Prof. Mussetta from Politecnico di Milano.The main contribution presented in the paper is divided in two parts:Optimum combination of input meteorological features using feature extraction techniqueLow […]
Ekanki Sharma
Investigating the Benefit of Time-Series Imaging for Load Disaggregation November 16, 2020
In this paper, we investigate the benefits of time-series imaging in load disaggregation, as we augment the wide-spread sequence-to-sequence approach by a key element: an imaging block. A Recurrence PlotThe approach presented in this paper converts an input sequence to an image, which in turn serves as input to a modified version of a common Denoising […]
Christoph Klemenjak
Evolving NILM to NIAD: Non-Intrusive Activity Detection October 20, 2020
Almost all documented practical use cases of load disaggregation rely on the analysis of appliance operational times and their impact on the monthly electricity bill. However, load disaggregation bears promising potential for other use cases. Recognizing user activities without the need to set up a dedicated sensing infrastructure is one such application, given that many […]
Christoph Klemenjak
Stop! Exploring Bayesian Surprise for Load Disaggregation October 13, 2020
In our latest paper, which is the result of the ongoing collaboration between our lab and SFU's Computational Sustainability Lab, we bring the concept of Bayesian Surprise to NILM. When has enough prior training been done? When has a NILM algorithm encountered new, unseen data? We apply the notion of Bayesian surprise to answer these important questions […]
Christoph Klemenjak
SYND - A Synthetic Energy Dataset August 3, 2020
As with related Machine Learning problems, applications like Non-Intrusive Load Monitoring (NILM) require a sufficient amount of data to train and validate new approaches. With SynD, we present a synthetic energy dataset emulating the power consumption of residential buildings. The dataset is freely available and contains 180 days of synthetic power data on aggregate level […]
Wil
Adaptive Weighted Recurrence Graph blocks for event-based NILM July 22, 2020
To this day, hyperparameter tuning remains a cumbersome task in Non-Intrusive Load Monitoring (NILM) research, as researchers and practitioners are forced to invest a considerable amount of time in this task.This paper proposes adaptive weighted recurrence graph blocks (AWRG) for appliance feature representation in event-based NILM. An AWRG block can be combined with traditional deep […]
Christoph Klemenjak
Supporting the Grid with Dynamic Residential Load Scheduling July 9, 2020
Demand response (DR) for smart grids intends to balance the required power demand with the available supply resources. This is especially important with an increased amount of renewable energy sources since for most of them the energy yield cannot be shifted in time. For example, photovoltaic systems will provide their peak power at noon while […]
Wil
Investigating Synthetic Data for Load Disaggregation June 25, 2020
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 […]
Christoph Klemenjak
Augmenting an Assisted Living Lab with Non-Intrusive Load Monitoring May 26, 2020
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:[youtube https://www.youtube.com/watch?v=YAcxwJa-HP8]In her talk, Hafsa Bousbiat describes how abnormal behavior can be […]
Bousbiat Hafsa
Reproducibility: Best practise examples March 25, 2020
In the blog article "Why it is important to share your code and make your paper accessible", we advocated for sharing source code together with papers that involve a simulation-based evaluation. One argument is that such papers will be of higher utility to the reader and therefore, will create higher impact.To help in this effort, […]
Wil