Robust NILM
Robust Machine learning methods for Non-Intrusive Load Monitoring (NILM)
Recently, large-scale deployments of smart meters have sparked the interest in developing effective non-intrusive load monitoring (NILM) solutions for improving energy monitoring, awareness and reducing energy consumption in buildings. NILM uses smart meter data to infer what end-appliances running in the building and estimate their respective power consumption. This project:
- Investigated and developed robust feature representation for appliance recognition at high frequency in NILM
- Developed robust multi-task and multilabel learning for appliance recognition in NILM and
- Develop end-to-end NILM and investigate its value propositions in smart-grid.