Non-intrusive load monitoring using machine learning and processed training data

A system for disaggregating energy usage associated with a target device, the system comprising:

• a processor;
• and
• a memory storing instructions for execution by the processor, the instructions configuring the processor to:
• store a trained machine learning model configured to disaggregate device energy usage from household energy usage, wherein a plurality of parameters of the machine learning model are trained to predict energy usage for a target device from household energy usage, and the plurality of parameters are trained using training data comprising energy usage values for a plurality of households, labeled energy usage values for the target device, and labeled energy usage values for a set of other devices;
• receive, as input data, household energy usage over a period of time, wherein the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices;
• and
• predict, using the trained machine learning model, energy usage for the target device over the period of time based on the received input data, wherein the predicting includes at least a progression of the input data through the trained machine learning model, the progression applying the trained plurality of parameters to the input data to generate the predicted energy usage for the target device.

A non-transitory computer readable medium having instructions stored thereon that, when executed by a processor, cause the processor to disaggregate energy usage associated with a target device, wherein, when executed, the instructions cause the processor to:

• store a trained machine learning model configured to disaggregate device energy usage from household energy usage, wherein a plurality of parameters of the machine learning model are trained to predict energy usage for a target device from household energy usage, and the plurality of parameters are trained using training data comprising energy usage values for a plurality of households, labeled energy usage values for the target device, and labeled energy usage values for a set of other devices;
• receive, as input data, household energy usage over a period of time, wherein the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices;
• and
• predict, using the trained machine learning model, energy usage for the target device over the period of time based on the received input data, wherein the predicting includes at least a progression of the input data through the trained machine learning model, the progression applying the trained plurality of parameters to the input data to generate the predicted energy usage for the target device.