Load Disaggregation Paper accepted in IEEE Transactions on Instrumentation and Measurement

Recently, the paper "PALDi: Online Load Disaggregation via Particle Filtering" got accepted for publication in the journal IEEE Transactions on Instrumentation and Measurement.

Egarter, D., Bhuvana, V. P. & Elmenreich W. (2014). PALDi: Online load disaggregation via particle filtering. IEEE Transactions on Instrumentation and Measurement, pages 467 - 477, 64(2).

Smart metering and fine-grained energy data are one of the major enablers for the future smart grid and improved energy efficiency in smart homes. By using the information provided by smart meter power draw, valuable information can be extracted as disaggregated appliance power draws by non-intrusive load monitoring (NILM). NILM allows to identify appliances according to their power characteristics in the total power consumption of a household, measured by one sensor, the smart meter.

In this paper we present a NILM approach, where the appliance states are estimated by particle filtering (PF). PF is used for non-linear and non-Gaussian disturbed problems and is suitable to estimate the appliance state. On/off appliances, multi-state appliances, or combinations of them are modeled by hidden Markov models (HMM) and their combinations result in a factorial hidden Markov model (FHMM) modeling the household power demand. We evaluate the PF-based NILM approach on synthetic and on real data from a well-known dataset to show that our approach achieves an accuracy of 90% on real household power draws.

GREEND: An Energy Consumption Dataset of Households in Italy and Austria

Some days ago, we got notice that our paper "GREEND: An Energy Consumption Dataset of Households in Italy and Austria" is accepted at the IEEE SmartGridComm 2014. The SmartGridComm 2014 will take place in Venice, a fascinating and marvelous Italian city, on November 3-6, 2014.

GREEND: An Energy Consumption Dataset of Households in Italy and Austria, 
Andrea Monacchi, Dominik Egarter, Wilfried Elmenreich, Salvatore D’Alessandro, Andrea M. Tonello

Home energy management systems can be used to monitor and optimize consumption and local production from renewable energy. To assess solutions before their deployment, researchers and designers of those systems demand for energy consumption datasets. In this paper, we present the GREEND dataset, containing detailed power usage information obtained through a measurement campaign in households in Austria and Italy. We provide a description of consumption scenarios and discuss design choices for the sensing infrastructure. Finally, we bench- mark the dataset with state-of-the-art techniques in load disaggregation, occupancy detection and appliance usage mining.

An authors' version is available in arXiv: http://arxiv.org/abs/1405.3100

The GREEND dataset is publicly availble, donwload the newest version at https://sourceforge.net/projects/greend/files/GREEND_0-2_300615.zip

Load Hiding of Household's Power Demand

Our publication "Load Hiding of Household's Power Demand" is accepted at the IEEE SmartGridComm 2014. The SmartGridComm 2014 will take place in Venice, a fascinating and marvelous Italian city, on November 3-6, 2014.

Load Hiding of Household's Power Demand, Dominik Egarter, Christoph Prokop, Wilfried Elmenreich

With the development and introduction of smart metering, the energy information for costumers will change from infrequent manual meter readings to fine-grained energy consumption data. On the one hand these fine-grained measurements will lead to an improvement in costumers’ energy habits, but on the other hand the fined-grained data produces in- formation about a household and also households’ inhabitants, which are the basis for many future privacy issues. To ensure household privacy and smart meter information owned by the household inhabitants, load hiding techniques were introduced to obfuscate the load demand visible at the household energy meter. In this work, a state-of-the-art battery- based load hiding (BLH) technique, which uses a controllable battery to disguise the power consumption and a novel load hiding technique called load-based load hiding (LLH) are presented. An LLH system uses an controllable household appliance to obfuscate the household’s power demand. We evaluate and compare both load hiding techniques on real household data and show that both techniques can strengthen household privacy but only LLH can increase appliance level privacy.

An authors' version is available in arXiv: http://arxiv.org/abs/1406.2534

 

Call for Papers 1st Workshop on Middleware for a Smarter Use of Electric Energy

CALL FOR PAPERS

MidSEE 2015

1st Workshop on Middleware for a Smarter Use of Electric Energy

in conjunction with NetSys 2015, Cottbus, Germany

https://www.netsys2015.com/workshops-tutorials/midsee/

MOTIVATION & WORKSHOP SCOPE

Smart energy meters are increasingly installed in newly constructed buildings worldwide, as regulated by, e.g., the European Union's Energy Services Directive. Their combination with the vision of an Internet of Things, in which billions of embedded devices will continually monitor the environment, enables the fine-grained monitoring of household and office energy usage and hence a large range of novel services. While many such functions have been devised so far (e.g., advice how to save energy or the disaggregation of electricity bills), these solutions commonly rely on the availability of hard- and software systems by a single manufacturer. Interoperability, including the potential to integrate new energy metering devices, is mostly unsupported due to the wide variety of (non-compatible) standards and communication protocols.

This workshop provides a platform to present current research activities in communications and processing of energy consumption data, with its primary focus on electric energy usage. The scope of the workshop includes contributions that address the integration of energy metering equipment from different manufacturers into a single middleware system which operates on a building level, as well as novel means to collect, process and visualize these data. Accepted papers will not only present novel research results in these domains, but also comprise an evaluation in which their merits are assessed.

Topics of interest include, but are not limited to:

• Novel energy metering hardware designs and experiences
• Data collection and command distribution protocols for energy  management systems
• Middleware systems for energy data, including novel local data processing algorithms
• Control of alternative energy sources to increase their production efficiency
• Innovative tools to model and visualize energy expenditure and production
• Sensing and actuation of electrical loads
• Privacy and security in energy management middleware systems
• User interfaces for energy display, management and control

PAPER SUBMISSION

Authors are invited to submit papers of up to 6 pages in IEEE two-column format. Submissions must be original and should not have been published previously or be under consideration for publication while being evaluated for this workshop. Accepted papers will be published in the IEEE Xplore Digital Library. More details about the ubmission process can be found on the website.


IMPORTANT DATES

Submission:   Sep. 26th, 2014
Notification: Nov. 17th, 2014
Camera Ready: Dec. 10th, 2014
Workshop:     Mar. 12th, 2015


WORKSHOP ORGANIZERS

Andreas Reinhardt, The University of New South Wales, Australia
Christian Renner, Universität zu Lübeck, Germany
Delphine Christin, University of Bonn & Fraunhofer FKIE, Germany


PROGRAM COMMITTEE

Matteo Ceriotti, University of Duisburg-Essen, Germany
Alexander De Luca, LMU München, Germany
Paul Dunphy, Newcastle University, UK
Wilfried Elmenreich, Alpen-Adria-Universität Klagenfurt, Austria
Stefan Fischer, Universität zu Lübeck, Germany
Hannes Frey, University of Koblenz-Landau, Germany
Stefan Katzenbeisser, TU Darmstadt, Germany
Marco Ortolani, University of Palermo, Italy
Oliver Parson, University of Southampton, UK
Daniele Puccinelli, SUPSI, Switzerland
Christian Steger, TU Graz, Austria
Volker Turau, TU Hamburg-Harburg, Germany
Sebastian Zöller, TU Darmstadt, Germany