- An Experimental Comparison of Performance Metrics for Event Detection Algorithms in NILM
- Real-Time Itemized Electricity Consumption Intelligence for Military Bases
- Scalable Energy Breakdown Across Regions
- Pecan Street's Dataport
- Making Sense of UK smart meter data
- Poster Lightning Talks
- A Practical Discussion of Lessons Learned Deploying Disaggregation at Broad Scale
- An Emulator for NILM and Smart Home Research
- Panel Discussion on NILM Outlook & Next-Generation Applications
- On the Feasibility of Generic Deep Disaggregation for Single-Load Extraction
- FactorNet for Energy Disaggregation
- Post-processing for Event-based Non-intrusive Load Monitoring
- Power Signature Obfuscation using Flexible Building Loads
My name is Oliver Parson, and I'm currently employed as a Senior Data Scientist at Bulb. I'm interested in investigating the ways in which machine learning can be used to break down household energy consumption data into individual appliances, also known as Non-intrusive Appliance Load Monitoring (NILM) or energy disaggregation.
Wednesday 9 May 2018
Presentation videos from NILM 2018 in Austin now available
I only recently noticed that videos from NILM 2018 in Austin are now available as a YouTube playlist. Here's a full list of the videos:
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