Hierarchical Temporal Memory-Based One-Pass Learning for Real-Time Anomaly Detection and Simultaneous Data Prediction in Smart Grids

A neuro-cognitive inspired architecture based on the Hierarchical Temporal Memory (HTM) is proposed for anomaly detection and simultaneous data prediction in real-time for smart grid
$\mu$
PMU data. The key technical idea is that the HTM learns a
sparse distributed temporal representation
of sequential data that turns out to be very useful for anomaly detection and simultaneous data prediction in real-time. Our results show that the proposed HTM can predict anomalies within 83–90 percent accuracy for three different application profiles, namely
Standard, Reward Few False Positive, Reward Few False Negative
for two different datasets. We show that the HTM is competitive to five state-of-the-art algorithms for anomaly detection. Moreover, for the multi-step prediction in the online setting, the same HTM achieves a low 0.0001 normalized mean square error, a low negative log-likelihood score of 1.5 and is also competitive to six state-of-the-art prediction algorithms. We demonstrate that the same HTM model can be used for
both the tasks
and can learn online in one-pass, in an unsupervised fashion and adapt to changing statistics.
The other state-of-the-art algorithms are either less accurate or are limited to one of the tasks or cannot learn online in one-pass, and adapt to changing statistics.