Intel Corporation

Pranav Sanghadia

Mats Gustav Agerstam

Mateo Guzman

Sindhu Pandian

Yatish Mishra

Shubhangi Rajasekhar

Troy Willes

Tomm Aldridge

James Y Song

Methods, apparatus, systems and articles of manufacture to trigger calibration of a sensor node using machine learning are disclosed. An example apparatus includes a machine learning model trainer to train a machine learning model using first sensor data collected from a sensor node. A disturbance forecaster is to, using the machine learning model and second sensor data, forecast a temporal disturbance to a communication of the sensor node. A communications processor is to transmit a first calibration trigger in response to a determination that a start of the temporal disturbance is forecasted and a determination that a first calibration trigger has not been sent.

Methods and apparatus to trigger calibration of a sensor node using machine learning

Methods and systems may provide for receiving a set of voltage samples corresponding to a line voltage of a leg in an electrical system and conducting a time domain analysis to identify one or more loads in the electrical system. Additionally, the time domain analysis may be used to identify one or more loads in the electrical system. In one example, conducting the time domain analysis involves classifying events with respect to cross correlation, shape magnitude, transient amplitude, transient duration, run delta and/or standard deviation.

Electrical load identification using system line voltage

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Chandler, AZ, United States of America

Pranav Sanghadia