An improved AC power supply is described. The supply identifies the load through monitoring the current and voltage wave forms and phase relations with the AC Mains. The comparison is done in conditions where the power to the load is programmably varied through use of a control switch located in the line and neutral between the AC mains and the load. The program of controlling the switch is varied to optimize the ability to distinguish similar load types. The switch can be further used to control power to the load that varies according to a set of rules based upon the identity of the load. In a preferred embodiment, the design enables high efficiency with minimal components that may be fully integrated onto silicon.

In an electronic apparatus, reference information stored on a memory includes standard appliance ownership information describing owned home appliances of at least one standard household and standard monthly power consumption information of the standard household. A processor obtains first monthly power consumption information of a first household which owns a first home appliance of a same type as a standard home appliance described in the standard appliance ownership information. Based on a difference between the standard and first monthly power consumption information being within a threshold range, the processor compares appliance monthly power consumption information of the standard home appliance and of the first home appliance, and, based on the result being within a threshold range, predicts first appliance ownership information on owned home appliances of the first household based on the standard appliance ownership information. The processor then provides guide information associated with the first appliance ownership information.

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.

This disclosure relates generally to methods and systems for determining the power load disaggregation profile of a building. Most of the conventional techniques are algorithmic centric, specific to certain scenarios and does not employ the low-sampling rate data due to the complexity involved. Present disclosure determines the power load disaggregation profile of the building using the low-sampling rate power consumption data accurately. According to the present disclosure, firstly, the background power loads are detected and removed from the low-sampled data samples. Next, a robust event detection mechanism is employed to detect the events when the change in the power consumption occurred, and such events are paired using the iterative pairing technique. Further, a set of event clusters are formed using the density-based clustering technique and lastly, each of the set of event clusters are classified with each appliance type using a rule-based classification technique.

An information processing apparatus comprises an acquisition unit configured to acquire information according to the usage circumstances of the portable electric generator; an estimation unit configured to estimate a type of an electrical device that is connected, based on a measurement result with respect to a voltage and a current when the portable electric generator supplied electric power that is included in the information according to the usage circumstances; and a calculation unit configured to calculate a rental fee associated with an electric power amount supplied by the portable electric generator at the rental destination, using a billing coefficient corresponding to an electrical device estimated by the estimation unit, and the measurement result.

Disclosed herein is a method for facilitating managing interconnection processes on a power transmission network. Accordingly, the method may include receiving a request from a device, retrieving a transmission network data associated with a power transmission network from a distributed ledger, analyzing the request and the transmission network data, generating an updated transmission network data of the power transmission network based on the analyzing, transmitting the updated transmission network data to a second device, receiving a response corresponding to the updated transmission network data from the second device, analyzing the response using a machine learning model, generating a validation status of the request transmitting the validation status to the device and the second device, and storing at least one of the response, the updated transmission network data, the validation status, and the request in the distributed ledger.

Methods, Systems, and Computer program products can use grid telemetry to generate a fingerprint. One of these methods includes generating a first grid telemetry fingerprint based on characteristics of an electrical grid. The method also includes verifying a transaction based on the first grid telemetry fingerprint and a second grid telemetry fingerprint associated with the transaction.

A management device is connected to an AC power line provided with a plurality of connection ports, each of which can be connected to an electric device including a power storage unit. The management device includes a measurement unit configured to measure an electric signal on the AC power line, a determination unit configured to determine a type of the electric device connected to the AC power line via a connection port in the plurality of connection ports based on a measurement result of the measurement unit, and a first prediction unit configured to predict an amount of power supply on the AC power line due to discharge of the power storage unit based on a determination result of the determination unit.

A system and method for monitoring the health of a patient in a residential environment. An energy monitoring module is in communication with an electrical panel providing power to electrical devices in the residential environment. The energy monitoring module is operable to monitor energy consumed by the electrical devices. A data energy analysis engine collects energy data from the energy monitoring module over a period of time. A health data correlation engine determines a health condition of the patient based on the collected energy data.

The present invention provides an apparatus and method enabling to perform a 2-dimensional trajectory analysis applicable for a three-phase system. A signal analysis apparatus generates a three-row and N-column waveform matrix constituted by three N dimensional row vectors having respectively N samples of first to third phase current values obtained from three-phase current signals measured in a three-phase system, where N is the number of samples of each of the three-phase current signals in one cycle of an AC power supply; applies transformation to the waveform matrix to obtain a two-row and N-column matrix constituted by the first and second N dimensional row vectors; performs normalization in amplitude of the first and second N dimensional row vectors, respectively; selects a grid size based on the sample number N; and maps a two dimensional trajectory made up from the first and second normalized N dimensional row vectors on a grid with the grid size selected.