Stephen Makonin's picture
Real name: 
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First Name: 
Stephen
Last Name: 
Makonin
Affiliation: 
Simon Fraser University
Job Title: 
Adjunct Professor
Expertise: 
Data Engineering, Software Engineering
Short Bio: 
I am an Adjunct Professor in Engineering Science, the Principal Investigator of the Computational Sustainability Lab, and Head Instructor of the Big Data Hub at Simon Fraser University (SFU). I received my PhD in Computing Science at Simon Fraser University in 2014 in the area of computational sustainability. I have been a software engineer for over 25 years working for various local/international industry clients. I is a registered Professional Engineering (PEng) with Engineers and Geoscientists BC and a Senior Member of the IEEE. My research interests include computational sustainability and the understanding of socioeconomic issues that pertain to technological advancement. I am considered an expert in data engineering, software engineering, and a world-renowned researcher in non-intrusive load monitoring (NILM) and disaggregation. Currently, I am the Vice-Chair of the IEEE Signal Processing Society Vancouver Chapter and I sit on the IEEE DataPort Advisory Committee. I 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. Additionally, this year (2021) I became a Voting Member of the Big Data Governance and Metadata Management (BDGMM, P2957), a new standard being developed by the IEEE Standards Association (IEEE SA) and NIST.

Datasets & Competitions

This dataset has EV charging data from 2019 to the present day. SFU's Burnaby campus currently has two different types of Electric Vehicle Charging Stations on campus. There is no additional charge to use the station; however, the Permit or Daily Rate required in each lot remains in effect for the EV Reserved stalls.

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264 Views

The AMPds dataset has been release to help load disaggregation/NILM and eco-feedback researcher test their algorithms, models, systems, and prototypes. This dataset is intended to be multi-year capture of the consumption of my house. This dataset contains electricity, water, and natural gas measurements at one minute intervals. This dataset contains a total of 1,051,200 readings for 2 years of monitoring (from April/2012 to March/2014) per meter. There are a total of 21 power meters, 2 water meters (with additional appliance usage annotations), and 2 natural gas meters.

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1482 Views