The present invention is generally directed to methods of disaggregating low resolution whole-house energy consumption data. In accordance with some embodiments of the present invention, methods may include steps of: receiving at a processor the low resolution whole house profile; selectively communicating with a first database including non-electrical information; selectively communicating with a second database including training data; and determining by the processor based on the low resolution whole house profile, the non-electrical information and the training data, individual appliance load profiles for one or more appliances.

An electric energy supply management method includes having a certifier system define a reference electric power profile for an electric apparatus, and having the certifier system provide a device coupled to the electric apparats and to a socket that delivers electric energy provided by an electric energy supplier. The device is associated only to the electric apparatus through the reference electric power profile. The method also includes having a user of the electric apparatus couple the electric apparatus to the socket through the device, and having the device check that the electric apparatus is coupled to the socket by comparing a measured electric power profile of the electric apparatus to the reference electric power profile. If the check has a positive outcome, the method has the device collect measurements about the electric power used by the electric apparatus and certify them as energy consumptions of the electric apparatus.

A power plug for coupling an electrical appliance to an electrical power supply, comprising: a current sensor, configured to measure current supplied through the plug to the electrical appliance; a voltage sensor, configured to measure voltage supplied through the plug to the electrical appliance; a processor configured to: determine power consumption data from data relating to the current and voltage measurements made by the current and voltage sensors; and monitor the performance of the electrical appliance using the power consumption data; and a transmitter, configured to transmit information relating to the performance of the electrical appliance.

Embodiments implement non-intrusive load monitoring using machine learning. A trained convolutional neural network (CNN) can be stored, where the CNN includes a plurality of layers, and the CNN is trained to predict disaggregated target device energy usage data from within source location energy usage data based on training data including labeled energy usage data from a plurality of source locations. Input data can be received including energy usage data at a source location over a period of time. Disaggregated target device energy usage can be predicted, using the trained CNN, based on the input data.

Embodiments implement non-intrusive load monitoring using a novel learning scheme. A trained machine learning model configured to disaggregate device energy usage from household energy usage can be stored, where the machine learning model is trained to predict energy usage for a target device from household energy usage. Household energy usage over a period of time can be received, where the household energy usage includes energy consumed by the target device and energy consumed by a plurality of other devices. Using the trained machine learning model, energy usage for the target device over the period of time can be predicted based on the received household energy usage.

A site management system has a site management device located on a fielded site, which has a controller unit integral with a power provision unit, and the power provision unit receives an input voltage via a conductor cable and delivers power to one or more receptacles. Additionally, the system has a plurality of remote devices communicatively coupled to the site management device over a wireless network and at least one off-site computing device communicatively coupled to the site management device. Further, the system has a processor on the controller unit that communicatively couples with at least one remote device, receives data indicative of a unique identifier from the wireless remote device, and determines whether the unique identifier correlates with a remote device of an individual who is permissively on the fielded site. In addition, the processor transits data indicative of the individual and data indicative of whether the individual is permissively on the fielded site to the off-site computing device or a site manager's remote device.

An apparatus and method are used for disaggregating and classifying energy consumption data from the overall energy consumption data of a building. Both high frequency sampling data classified by a convolutional neural network running on a cloud server and low frequency sampling data analyzed by a convolutional neural net running locally on an electric smart meter are used in the method. A control circuit switches between high and low frequency sampling modes based on the activity level of the building. Individual devices can also be controlled through a mobile application connected to the control circuit.

A power source is proposed comprising an output for a load. The power source is configured to provide a first voltage at the output. The power source is configured to detect a feedback at the output. The power source is further configured to provide a second voltage at the output based on the detected feedback. A high second voltage is only provided if an appropriate load is detected. This has the advantage that people are protected from electric shock e.g. when handling unconnected cables.

A power-supply unit for supplying power to an electrical load, said load vaporizing or atomizing a flavor source or an aerosol source, comprises a power-supply, a control part and a connection part which electrically connects the power-supply to the load. The control part is configured to acquire a value relating to the electrical resistance of the load, either when a prescribed time elapses after supplying power to the load or on the basis of the change rate of the value relating to the electrical resistance of the load, the temperature of the load or the pulse number of the power supplied to the load.

The present system relates to a power topology mapping system for identifying which one of one or more equipment components are being powered from a specific phase of a multi-phase AC power source. The system makes use of a plurality of power receiving subsystems which each receive an AC power signal from at least one phase of the multi-phase AC power source. Each power receiving subsystem has a communications card, an identification designation unique to it, and a controller. One of the power receiving subsystems is designated as a reference power domain component. The controllers each carry out phase angle measurements associated with the AC power signal being received by its power receiving subsystem. A topology mapping subsystem is included which analyzes phase angle measurement data reported by the power receiving subsystems and determines which subsystem is being powered by which phase of the multi-phase AC signal.