SEDR-Seq2P: A Lightweight Dilated Residual Sequence-to-Point Network for Multi-Task Industrial NILM
SEDR-Seq2P is a lightweight Seq2Point extension using dilated residual blocks and squeeze-and-excitation attention for multi-task industrial NILM The model reduces MAE by ~7%, improves R² by ~1%, and increases match rate by ~0.8% compared to the Seq2Point baseline SEDR-Seq2P cuts inference latency by ~58% versus WaveNet while maintaining competitive accuracy Industrial NILM faces unique challenges from measurement noise and concurrent machine operations that reduce generalization from residentia
Analysis
TL;DR
- SEDR-Seq2P is a lightweight Seq2Point extension using dilated residual blocks and squeeze-and-excitation attention for multi-task industrial NILM
- The model reduces MAE by ~7%, improves R² by ~1%, and increases match rate by ~0.8% compared to the Seq2Point baseline
- SEDR-Seq2P cuts inference latency by ~58% versus WaveNet while maintaining competitive accuracy
- Industrial NILM faces unique challenges from measurement noise and concurrent machine operations that reduce generalization from residential-tuned models
- Unified benchmarking on IMDELD dataset across Seq2Seq, Seq2SubSeq, Seq2Point, GRU, and WaveNet reveals Seq2Point offers the best accuracy-delay balance among lightweight options
Why It Matters
This work addresses a critical gap in industrial energy monitoring, where non-intrusive load monitoring (NILM) models trained on residential data fail to generalize under noisy, high-concurrency industrial conditions. By delivering a lightweight yet accurate multi-task disaggregation model, SEDR-Seq2P enables scalable deployment of real-time energy monitoring systems in industrial settings without prohibitive computational costs.
Technical Details
- Architecture: SEDR-Seq2P extends the Seq2Point framework with dilated residual blocks for multi-scale feature extraction and squeeze-and-excitation (SE) attention for adaptive channel-wise feature recalibration
- Task formulation: One-to-many multi-task disaggregation where a single network estimates loads from multiple industrial machines simultaneously from aggregate power signals
- Benchmarking: Unified evaluation on the IMDELD dataset using energy-estimation metrics (MAE, R², match rate) and the accuracy-delay criterion, comparing against Seq2Seq, Seq2SubSeq, Seq2Point, GRU, and WaveNet baselines
- Performance gains: ~7% MAE reduction, ~1% R² improvement, ~0.8% match rate increase over Seq2Point baseline; ~58% latency reduction compared to WaveNet
- Key insight: Seq2Point architecture provides a stronger accuracy-delay trade-off than sequence-to-sequence variants, while SEDR enhancements close the accuracy gap with heavier models like WaveNet
Industry Insight
- Industrial NILM systems should prioritize accuracy-delay trade-offs over raw accuracy alone, as real-time deployment constraints often make lightweight models more practical than computationally expensive alternatives
- The squeeze-and-excitation attention mechanism proves effective for industrial power signal disaggregation, suggesting it could be adapted for other time-series decomposition tasks in energy and IoT domains
- Standardized benchmarking protocols like the one used on IMDELD are essential for fair model comparison and accelerating progress in industrial NILM research
Disclaimer: The above content is generated by AI and is for reference only.