Research map / Learning theory and algorithms / Combinatorial and search optimization
Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| expand episode search from length 2-3 to lengths 4 and 5 (ablation) | Iterative discovery · REDD | F-measure | Helped |
| expand episode search to lengths 4 and 5 (ablation) | Iterative discovery · BLUED | F-measure | Hurt |
| use ground truth events instead of Bayesian detected events (ablation) | Both methods · BLUED | F-measure | Helped |
| raise sampling rate of detected events from 1 Hz to 60 Hz (ablation) | Both methods · BLUED | F-measure | Hurt |
| iterative discovery instead of modified motif mining | Appliance discovery · REDD | F-measure | Helped |
| iterative discovery instead of modified motif mining | Appliance discovery · BLUED | F-measure | Mixed |
| iterative discovery instead of modified motif mining | Estimated energy per house · REDD houses H1-H6 | energy estimate accuracy | Mixed |
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