Mario Bergés, David Irwin, and I are excited to announce that the 5th International Workshop on Non-Intrusive Load Monitoring (NILM 2020) will be held at ACM BuildSys 2020. Due to the COVID-19 pandemic, the event will be completely virtual and take place on November 17th (the date may change depending on final decision from BuildSys organizers).
At the moment, we would like you to save the date and start planning your paper submissions. More information can be found in the attached Call for Papers, but the important dates are as follows:
- Abstract Registration: August 7
- Paper Submission Deadline: August 14
- Notification of Acceptance: September 18
- Final Paper Submission Due: October 2 (Tentative)
Our website will also maintain up-to-date information as we get closer to the event date: http://nilmworkshop.org/2020/
Please read our CFP.
We look forward to discussing all things NILM with you!
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Published by Stephen Makonin
Dr. Stephen Makonin is an Adjunct Professor in Engineering Science and the Principal Investigator of the Computational Sustainability Lab at Simon Fraser University (SFU). He received his PhD in Computing Science at Simon Fraser University in 2014 in the area of computational sustainability. He has been a software engineer for over 24 years working for various local/international industry clients. Stephen is a registered Professional Engineering (PEng) with Engineers and Geoscientists BC and a Senior Member of the IEEE. His research interests include computational sustainability and the understanding of socioeconomic issues that pertain to technological advancement. Stephen is an expert in data engineering, software engineering, and a world-renowned researcher in non-intrusive load monitoring (NILM) and disaggregation. Stephen is currently the Vice-Chair of the IEEE Signal Processing Society Vancouver Chapter and sits on the IEEE DataPort Advisory Committee. He currently serves as the Editor in Chief of the IEEE DataPort Metadata Review Board, and as an Editorial Board Member of Nature's Scientific Data journal.
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