The Note-Chord-Voice Framework: Structured Source Separation and Causal Inference for EV Charging Data
Real-world EV charging data exhibit three interlocking pathologies: hardware fragmentation (network timeouts and billing resets split sessions), physical violations (independent energy/duration models produce impossible states like 50 kWh in 10 min on a 7 kW charger), and collider bias (clustering on post-treatment outcomes opens backdoor paths for price elasticity). We propose the Note-Chord-Voice framework, a music-inspired, axiom-driven pipeline that separates data cleaning (Repair Chords), structural pattern discovery (Harmonic Chords), descriptive source separation (NMF Voices), and causal inference into distinct, falsifiable stages. Key innovations: (i) falsification gates (A1-A5, G...
| Authors | Jiajie Chen, Jinfeng Li |
|---|---|
| Primary category | eess.SP |
| Published | 14 August 2026 |
| Last updated at source | 14 August 2026 |
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