First IEEE Workshop on Pervasive Energy Services (PerEnergy)


A wealth of unexplored knowledge exists in the power data collected from smart meters and smart plugs (i.e. metering equipment for individual wall outlets). Analysis of the data may be used to provide context-based services (e.g. presence detection), but also serve as the foundation for novel services (e.g. automated recommendations for energy savings). To date, the potential of this domain has barely been explored, although more and more energy data is becoming available due to the increasing number of deployed smart metering equipment. At the moment, one of the most important research challenges in smart energy systems is thus the creation of value-added services that exploit the information content in energy generation and consumption data in such a way that they can inspire new functionalities and context models.

The aim of this workshop is thus to bring together practitioners and researchers from both academia and industry in order to have a forum for discussion and technical presentations on the recent advances in the innumerous opportunities opened up by the use of energy data in context-aware systems. It furthermore serves as a forum for the smart energy research community to discuss open issues, novel solutions and the future development of smart energy management systems in general.

 Topics of interest include, but are not limited to:

- Novel architectures for the integration of smart meter/smart plugs into pervasive computing systems

- Seamless energy meter integration in the design and architecture of smart home/office spaces

- Applications and novel business models that rely on energy data; including energy optimization and sustainable operation of smart cities, household load disaggregation, generation and load forecasting, and other means for informed load shifting

- Collection, processing, and integration of energy data to improve the control over loads and generation (e.g., distributed renewable sources)

- Security and privacy considerations in smart energy metering, including novel means to mitigate privacy and security threats

- Experiences with experimental power metering system prototypes and pilots

- Power metering testbeds and data sets for the comparison of household characteristics and processing approaches in a globalized world

- Industrial use cases showing gaps to be filled by future research

Submission instructions

Authors are invited to submit regular (full) papers for presentation at the workshop, describing original, previously unpublished work, which is not currently under review by another workshop, conference, or journal. Regular papers should present novel perspectives within the general scope of the workshop.

Papers may be no more than 6 pages in length. Papers in excess of page limits shall not be considered for review or publication. All papers must be typeset in double-column IEEE format using 10pt fonts on US letter paper, with all fonts embedded. Submissions must be made via EDAS:

It is a requirement that all authors listed in the submitted paper are also listed in EDAS. The author section of EDAS will be locked after the workshop submission deadline to ensure that conflict-of-interest can be properly enforced during review. If the list of authors differs between the paper and EDAS, the paper may not be reviewed. The authors of accepted papers must guarantee that their paper will be presented at the workshop. At least one author of each accepted paper must be registered at full rate for that paper to appear in the proceedings and to be scheduled for presentation.

Each accepted paper will require a full PERCOM registration (no registration is available for workshops only).

Call for Papers as undefinedtext

Important dates

Manuscript Submission: 7 December 2014 (extended deadiine)

Acceptance Notification: 13 January 2015

Final Manuscript: 28 January 2015

Workshop: 23 March 2015


  • Workshop co-chairs:

undefinedAndreas Reinhardt, TU Clausthal, Germany

undefinedFrank Dürr, University of Stuttgart, Germany

Delphine Christin, University of Bonn, Fraunhofer FKIE, Germany

  • Technical Program Committee:

Oliver Amft, University of Passau

Christian Beckel, ETH Zurich

Mario Berges, Carnegie Mellon University

Hermann de Meer, University of Passau

Wilfried Elmenreich, University of Klagenfurt

Stefan Katzenbeisser, Technische Universität Darmstadt

Georgios Konstantinou, The University of New South Wales

Stephen Makonin, Simon Fraser University

Oliver Parson, University of Southampton

Joern Ploennigs, IBM Research

Anita Sobe, University of Neuchatel

Mariya Sodenkamp, Otto-Friedrich-Universität Bamberg

Jay Taneja, IBM Research Lab Nairobi

Steffen Wendzel, Fraunhofer FKIE

Workshop program


Session 1 "Welcome and keynote address"

08.30-08.40 Welcome message by the chairs

08.40-09.30 Keynote "Green Building Energy Analytics: Challenges and Opportunities" by Nirmalya Roy (University of Maryland Baltimore County, USA)

Abstract: Green building applications need efficient and finegrained determination of power consumption pattern of a wide variety of consumer-grade appliances through non-intrusive load monitoring (NILM) techniques. Fine-grained monitoring of everyday appliances can provide better feedback to the consumers and motivate them to change behavior in order to reduce their energy usage. It also helps to detect abnormal power consumption events, long-term appliance malfunctions and potential safety concerns. Commercially available plug meters can be used for individual appliance monitoring but for an entire house, each such individual plug meters are expensive and tedious to setup. In practice, deploying smart plug based NILM and acquiring the low-level power measures of a large number of devices is often difficult or impossible due to the deployment complexity and varying characteristics of devices and thus must instead be employed at the circuit or house-level and inferred through the incorporation of novel usage-based measurement and probabilistic level-based disaggregation algorithm. But the challenges in deploying non-intrusive load monitoring algorithm involve disaggregating individual devices consumption from the aggregate power measurement, as well as modeling and incorporating the usage based prediction. In this talk, I will discuss on advanced machine learning and data analytics algorithms that capture the measurement based approach and circuit level NILM with the autonomous profiling and prediction logic, and the significant practical impact of intelligent use of such profiling techniques for green building applications. Our approach help improve the performance of energy disaggregation algorithms and provide critical insights on appliance longevity, abnormal power consumption, consumer behavior and their everyday lifestyle activities. The performance of our proposed algorithms on real data traces will be presented. I will conclude this talk with our ongoing research projects in this area and future research directions.

Biography: Nirmalya Roy is currently an Assistant Professor in the Information Systems department at University of Maryland Baltimore County. He directs the Mobile, Pervasive and Sensor Computing Group (MPSC) at University of Maryland Baltimore County. He was a Clinical Assistant Professor in the School of Electrical Engineering and Computer Science at Washington State University from January 2012 to June 2013. Prior to that, he worked as a Research Scientist at Institute for Infocomm Research (I2R), Singapore from 2010 to 2011. He was as a postdoctoral fellow in Electrical and Computer Engineering Department at The University of Texas at Austin from 2008 to 2009. He received his Ph.D. and M.S. in Computer Science and Engineering from The University of Texas at Arlington in 2008 and 2004 respectively. He did his Bachelors in Computer Science and Engineering from Jadavpur University, India in 2001.

Session 2 "Disaggregation and load monitoring" 

09.30-09.50 Autonomous Load Disaggregation Approach based on Active Power Measurements by Dominik Egarter and Wilfried Elmenreich (University of Klagenfurt, Austria)

09.50-10.10 L1-norm Minimization Based Algorithm for Non-Intrusive Load Monitoring by Xuan-Chien Le (IRISA, INRIA, University of Rennes 1, France), Baptiste Vrigneau (University of Rennes 1 & INRIA/IRISA CAIRN, France), and Olivier Sentieys (University of Rennes 1, IRIA/IRISA, France)

10.10-10.30 Coffee break

 Session 3 "Pervasive energy services"

10.30-10.50 Contextual Insights into Home Energy Relationships by Germaine Irwin (University of Maryland Baltimore County, USA), Nilanjan Banerjee (University of Maryland, Baltimore County, USA), Amy Hurst (UMBC, USA), and Sami Rollins (University of San Francisco, USA)

10.50-11.10 Enabling Consumer Behavior Modification through Real Time Energy Pricing by Xing Yan, Dustin Wright, Sunil Kumar, Gordon Lee and Yusuf Ozturk (San Diego State University, USA)

11.10-11.30 An intervention study on automated lighting control to save energy in open space offices by Luis I Lopera Gonzalez (University of Passau, Germany), Ulf Großekathöfer (Holst Centre & Imec, The Netherlands), and Oliver Amft (University of Passau, Germany)

11.30-11.50 User Interaction event detection in the context of appliance monitoring by Antonio Ridi (University of Applied Sciences Western Switzerland Fribourg, Switzerland), Christophe Gisler (University of Applied Sciences of Western Switzerland & University of Fribourg, Switzerland), and Jean Hennebert (University of Fribourg, Switzerland)

11.50-12.00 Closing message by the chairs