Research map / Probabilistic and scientific modeling / Variational and generative sampling
Deep Latent Generative Models for Energy Disaggregation
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| use VRNN-DIS-ALL instead of +Context (Tomkins et al. best model) | energy disaggregation · DATAPORT | MAE: 29% lower MAE | Helped |
| use VRNN-DIS-ALL instead of ADMM-RR | energy disaggregation · DATAPORT | MAE: 41% lower MAE | Helped |
| use VRNN-DIS-ALL instead of ADMM-RR | energy disaggregation · REDD | MAE: 41% lower MAE | Helped |
| use VRNN-DIS-ALL instead of Tomkins et al. models | energy disaggregation · REDD | MAE | No clear effect |
| VRNN-DIS-ALL vs ADMM-RR on dishwasher and lights | energy disaggregation · REDD | MAE: 69% and 68% improvement | Helped |
| test on a held-out building instead of training on same building (ablation) | VRNN-DIS-ALL · REDD | MAE avg: 25.17 -> 35.54 | Hurt |
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