This month I found out that a conference paper I helped co-author with colleagues Chinthaka Dinesh and Ivan V. Bajić was accepted for presentation. Titled Time-Of-Day Usage Patterns Into Non-Intrusive Load Monitoring, it will be presented at the 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP) this November in Montreal, Canada. Here is the paper abstract:
Understanding appliance power consumption can help occupants optimize their power consumption behaviour. One popular class of methods for determining appliance power consumption is known as non-intrusive load monitoring (NILM). This paper shows how to incorporate time-of-day appliance usage patterns into a recent NILM method, resulting in both improved accuracy and reduction in computational complexity.
Keywords: non-intrusive load monitoring (NILM), time-of-day usage, smart grid, load disaggregation, graph spectral representation
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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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