AI / ML research · 2020
Non-Intrusive Load Monitoring (NILM)
Final-year project: a single-sensor system that identifies appliances from their unique load signatures via ML classification.
- Machine Learning
- Signal Processing
- Classification
The problem
Identify which appliances are running from a single aggregate sensor.
My role
Final-year undergraduate project.
What I built
Built a single-sensor NILM pipeline that disaggregates appliance-level usage from unique load signatures using ML classification — end to end, from signal acquisition to appliance-level prediction.
Architecture & stack
- Single-sensor signal acquisition
- Load-signature feature extraction
- ML classification for appliance disaggregation