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arXiv Machine Learning By Tianhang Tan, Han Wu, Tousif Rahman, Shengyu Duan, Alex Yakovlev, Rishad Shafik

A Real-Time Tsetlin Machine-based Non-intrusive Load Monitoring System on MCUs

Read the original on arXiv Machine Learning →

The paper introduces a Tsetlin Machine (TM)-based framework for Non‑Intrusive Load Monitoring (NILM) that can run in real time on resource‑constrained microcontrollers. By reformulating NILM as a classification problem, the authors achieve high accuracy—90 % precision and 96 % recall for two appliances, and 77 % precision and 80 % recall for four appliances on the REDD dataset—while keeping the trained model to only 18 KB of flash memory. On an ESP32, the system delivers inference in 0.43 ms, demonstrating its feasibility for privacy‑preserving edge deployment.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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