Abstract
The accurate detection of appliance state transitions in electrical signals is fundamental for numerous energy-conserving applications. We present an extensive overview and categorization of the current state in event detection on high-sampling-rate signals. Existing approaches are designed for specific environments and need to be tediously adapted for new ones. Thus, we propose an unsupervised, multi-environment event detector, outperforming four state-of-the-art algorithms on two heterogeneous public datasets.
| Original language | English |
|---|---|
| Pages (from-to) | 703-709 |
| Number of pages | 7 |
| Journal | IEEE Transactions on Sustainable Computing |
| Volume | 6 |
| Issue number | 4 |
| DOIs | |
| State | Published - 2021 |
UN SDGs
This output contributes to the following UN Sustainable Development Goals (SDGs)
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SDG 7 Affordable and Clean Energy
Keywords
- Energy-aware systems
- Event detection
- Machine learning
- Neural nets
- Non-intrusive load monitoring
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