Computational Sustainability

Environment, Economy & Society

Computational sustainability tries to balance the needs of the environment, the economy, and society to solve sustainability problems using computational methods such as algorithms. Our lab is particularly interested in solving climate change and the energy crisis through the theme of conservation and habitual change. To this end, we work with both academic and industry partners to develop and commercialize our discoveries and solutions.

If we do not provide way to mitigate climate change and to decrease our ever-growing energy demands, we will leave a legacy of neglect that will negatively impact our children and future generations to inhabit this great planet, Earth.

“I have lived in the province of British Columbia (Canada) all my life. I grew up in Glade outside Castlegar. When I was young, I was surrounded by mountains, forests, creeks, and rivers. I explored them during the hot summer days and even during the cold of winter. My growing up with nature has shaped my passion and motivated my research into computational sustainability.” — Dr. Stephen Makonin, Lab Principal Investigator

Lab Projects

Our goal is to work with our SFU departments and external partners to help understand what computation tools are available and how computation can be used to solve difficult suitability problems. We apply and invent new machine learning and statistical signal processing algorithms to the many different aspects of computational sustainability. Here are some of our current projects.

Non-Intrusive Load Monitoring (NILM)

Use a smart meter for a house, building, microgrid, etc., to infer and track the performance of appliances and loads; thus, helping to create a smart energy efficient environment.

Data Engineering & Data Collection

Collect data and publish publicly available datasets that are used to test machine learning systems that attempt to solve sustainability problems.

Community Power & Smart Grid

Investigating how communities can become self-reliant by generating and sharing power using new information and communication technologies (ICT).

Personal Carbon Tracking

Create a ubiquitous computing platform that communities can use to track their carbon output and collectively reduce their carbon footprint.

Lab Members

FACULTY & ADVISORS

Kelly Graves

External Member
Industry Advisor

HQP (STUDENTS & POSTDOCS)

Md. Zulfiquar Ali Bhotto

Postdoctoral Fellow
Researcher
Engineering Science

Alejandro Rodriguez-Silva

PhD Candidate
Researcher
Engineering Science

Richard Jones

MASc Candidate
Researcher
Engineering Science

Alon Harell

MASc Candidate
Researcher
Engineering Science

Chinthaka Dinesh

PhD Candidate
Associate Researcher
Engineering Science

Chris Hynes

PhD Candidate
Associate Researcher
Engineering Science

VISITING SCHOLARS

Christoph Klemenjak

Visiting PhD Scholar
University of Klagenfurt
Austria

David Murray

Visiting PhD Scholar
University of Strathclyde
United Kingdom

Bundit Buddhahai

Visiting PhD Scholar
King Mongkut's University of Technology Thonburi: KMUTT
Thailand

Shikha Singh

Visiting PhD Scholar
Indraprastha Institute of Information Technology (IIIT), Delhi
India

Megha Gaur

Visiting PhD Scholar
Indraprastha Institute of Information Technology (IIIT), Delhi
India

Select Publications

BOOKS

Fernando Pacheco-Torgal, Erik Rasmussen, Claes-Göran Granqvist, Volodymyr Ivanov, Arturas Kaklauskas, Stephen Makonin (editors). Start-Up Creation: The Smart Eco-Efficient Built Environment. Woodhead Publishing/Elsevier, ISBN: 978-0-08-100546-0, 2016.

JOURNAL ARTICLES (PEER REVIEWED)

Stephen Makonin. HUE: The Hourly Usage of Energy Dataset for Buildings in British Columbia. Data in Brief, vol. 23, no. 103744 , pp. 1-4 (2019).
Megha Gaur, Stephen Makonin, Ivan V. Bajić, Angshul Majumdar. Performance evaluation of techniques for identifying abnormal energy consumption in buildings. IEEE Access, vol. 7, no. 103744 , pp. 62721–62733 (2019).
Chinthaka Dinesh, Stephen Makonin, Ivan V. Bajić. Residential Power Forecasting Using Load Identification and Graph Spectral Clustering. IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 66, no. 11 , pp. 1900-1904 (2019).
Stephen Makonin, Z. Jane Wang, Chris Tumpach. RAE: The Rainforest Automation Energy Dataset for Smart Grid Meter Data Analysis. Data, vol. 3, no. 1 , pp. 1-9 (2018).
Stephen Makonin, Bradley Ellert, Ivan V. Bajić, Fred Popowich. Electricity, water, and natural gas consumption of a residential house in Canada from 2012 to 2014. Scientific Data, vol. 3, no. 160037 , pp. 1-12 (2016).
Md. Zulfiquar Ali Bhotto, Stephen Makonin, Ivan V. Bajić. Load disaggregation based on aided linear integer programming. IEEE Transactions on Circuits and Systems II: Express Briefs, vol. 64, no. 7, pp. 792-796 (2016).
Stephen Makonin, Fred Popowich, Ivan V. Bajić, Bob Gill, Lyn Bartram. Exploiting HMM Sparsity to Perform Online Real-Time Nonintrusive Load Monitoring. IEEE Transactions on Smart Grid, vol. 7, no. 6, pp. 2575-2585 (2015).
Stephen Makonin, Fred Popowich. Nonintrusive Load Monitoring (NILM) Performance Evaluation. Energy Efficiency, vol. 8, no. 4, pp. 809–814 (2015).
Stephen Makonin, Lyn Bartram, Fred Popowich. A Smarter Smart Home: Case Studies of Ambient Intelligence. IEEE Pervasive Computing, vol. 12, no. 1, pp. 58–66 (2013).

CONFERENCE PROCEEDINGS (PEER REVIEWED)

Richard Jones, Alejandro Rodriguez-Silva, Stephen Makonin. Increasing the Accuracy and Speed of Universal Non- Intrusive Load Monitoring (UNILM) Using a Novel Real-Time Steady-State Block Filter. Proceedings of the 11th Conference on Innovative Smart Grid Technologies (ISGT), Washington DC, USA, February 2020.
Christoph Klemenjak, Stephen Makonin, Wilfried Elmenreich. Towards Comparability in Non-Intrusive Load Monitoring: On Data and Performance Evaluation. Proceedings of the 11th Conference on Innovative Smart Grid Technologies (ISGT), Washington DC, USA, February 2020.
Alejandro Rodriguez-Silva, Stephen Makonin. Universal Non-Intrusive Load Monitoring (UNILM) Using Filter Pipelines, Probabilistic Knapsack, and Labelled Partition Maps. Proceedings of the 11th IEEE PES Asia-Pacific Power and Energy Engineering Conference 2019 (APPEEC), Macau, China, December 2019.
Christoph Klemenjak, Andreas Reinhardt, Lucas Pereira, Stephen Makonin, Mario Bergés, Wilfried Elmenreich. Electricity Consumption Data Sets: Pitfalls and Opportunities. Proceedings of the 6th ACM International Conference on Systems for Energy-Efficient Built Environments, Cities, and Transportation (BuildSys), New York, USA, November 2019.
Laura Guzman, Stephen Makonin, Roger Alex Clapp. CarbonKit: Designing A Personal Carbon Tracking Platform. Proceedings of SocialSense ’19: Fourth International Workshop on Social Sensing, Montréal, Canada, April 2019.
Alon Harell, Stephen Makonin, Ivan V. Bajić. WaveNILM: A Causal Neural Network for Power Disaggregation from the Complex Power Signal. Proceedings of the 44th International Conference on Acoustics, Speech, and Signal Processing (ICASSP), Brighton, United Kingdom, May 2019.
Chinthaka Dinesh, Stephen Makonin, Ivan V. Bajić. Incorporating Time-Of-Day Usage Patterns Into Non-Intrusive Load Monitoring. Proceedings of the 5th IEEE Global Conference on Signal and Information Processing (GlobalSIP), Montréal, Canada, November 2017.
Bradley Ellert, Stephen Makonin, Fred Popowich. Appliance Water Disaggregation via Non-Intrusive Load Monitoring (NILM). Proceedings of the EAI International Conference on Big Data and Analytics for Smart Cities (BigDASC), Toronto, Canada, October 2015.
Stephen Makonin, Laura Guzman Flores, Robyn Gill, Roger Alex Clapp, Lyn Bartram, Bob Gill. A Consumer Bill of Rights for Energy Conservation. Proceedings of the IEEE Canada International Humanitarian Technology Conference (IHTC), Montréal, Canada, June 2014.