Restricted boltzmann machine based source-separation model with application to load disaggregation

A system, comprising:

• a memory storing instructions;
• one or more communication interfaces;
• and
• one or more hardware processors coupled to the memory via the one or more communication interfaces, wherein the one or more hardware processors are configured by the instructions to:
• receive an input comprising power consumption of a first set of appliances deployed in an infrastructure for a specific time period;
• obtain information pertaining one or more power consumption patterns specific to a second set of appliances;
• learn, via a Restricted Boltzmann Machine (RBM) executed by the one or more hardware processors, (i) one or more representations of one or more appliances of interest (AoI), wherein the one or more AoI are subset of at least one of the first set of appliances and the second set of appliances, wherein each softmax layer of the RBM learns a representation of a corresponding AoI from the one or more AoI based on a discriminative output obtained from remaining one or more softmax layers of the RBM;
• map, via one or more full connected layers of a neural network executed by the one or more hardware processors, the one or more learned representations to a corresponding power consumption pattern from the one or more power consumption patterns to obtain one or more mapped appliance consumption patterns for each of the one or more AoI;
• and
• estimate, via the one or more hardware processors, one or more appliance signatures for the one or more AoI based on the one or more mapped appliance consumption patterns.

One or more non-transitory machine readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors causes load disaggregation of appliances of interest by:

• receiving, via the one or more hardware processors, an input comprising power consumption of a first set of appliances deployed in an infrastructure for a specific time period;
• obtaining, via the one or more hardware processors, information pertaining one or more power consumption patterns specific to a second set of appliances;
• learning, via a Restricted Boltzmann Machine (RBM) executed by the one or more hardware processors, (i) one or more representations of one or more appliances of interest (AoI), wherein the one or more AoI are subset of at least one of the first set of appliances and the second set of appliances, wherein each softmax layer comprised in the RBM learns a representation of a corresponding AoI from the one or more AoI based on a discriminative output obtained from remaining one or more softmax layers of the RBM;
• mapping, via one or more full connected layers of a neural network executed by the one or more hardware processors, the one or more learned representations to a corresponding power consumption pattern from the one or more power consumption patterns to obtain one or more mapped appliance consumption patterns for each of the one or more AoI;
• and
• estimating, via the one or more hardware processors, one or more appliance signatures for the one or more AoI based on the one or more mapped appliance consumption patterns.