Research map / Architectures and efficiency / RNNs and normalization
Sequence-to-Point Learning With Neural Networks for Non-Intrusive Load Monitoring
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Measured effects reported in the paper
| What was changed | Where | Measured | Effect |
|---|---|---|---|
| Predict only the midpoint of each input window instead of the full output window | CNN NILM model · UK-DALE | MAE (Watt), overall: 17.999 -> 15.472 | Helped |
| Predict only the midpoint of each input window instead of the full output window | CNN NILM model · UK-DALE | SAE, overall: 0.423 -> 0.321 | Helped |
| Predict only the midpoint of each input window instead of the full output window | CNN NILM model · REDD | MAE (Watt), overall: 26.552 -> 23.693 | Helped |
| Predict only the midpoint of each input window instead of the full output window | CNN NILM model · REDD | SAE, overall: 0.355 -> 0.270 | Helped |
| Use the deeper CNN and all sliding-window training samples rather than the Kelly seq2seq setup | Seq2seq · UK-DALE | MAE (Watt), overall: 93.488 ± 91.112 -> 17.999 ± 9.063 | Helped |
| Use the deeper CNN and all sliding-window training samples rather than the Kelly seq2seq setup | Seq2seq · UK-DALE | SAE, overall: 4.001 ± 5.124 -> 0.423 ± 0.194 | Helped |
Closest papers by meaning
- Subtask Gated Networks for Non-Intrusive Load Monitoring (AAAI 2019 · similarity 0.92)
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- Model-Free Iterative Temporal Appliance Discovery for Unsupervised Electricity Disaggregation (AAAI 2018 · similarity 0.88)
- Deep Latent Generative Models for Energy Disaggregation (AAAI 2019 · similarity 0.88)
- PowerPM: Foundation Model for Power Systems (NeurIPS 2024 · similarity 0.84)
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