A method for monitoring energy-related data in an electrical system includes processing energy-related data from or derived from energy-related signals captured by at least one intelligent electronic device in the electrical system to identify at least one variation/change in the energy-related signals. The method also includes determining if the at least one identified variation/change meets a prescribed threshold or thresholds, and in response to the at least one identified variation/change meeting the prescribed threshold or thresholds, characterizing and/or quantifying the at least one identified variation/change. Information related to the characterized and/or quantified at least one identified variation/change is appended to time-series information associated with the energy-related data, and characteristics and/or quantities associated with the time-series information are evaluated to identify at least one potential load type associated with the characterized and/or quantified at least one identified variation/change.

Example electric appliance identification methods and apparatuses are provided. One example method includes obtaining, by a power line communication (PLC) device, a first noise signal in a circuit. The PLC device can then obtain first data based on the first noise signal, where the first data is used to describe a time-frequency feature of the first noise signal. The PLC device can then obtain, based on an electric appliance identification model and the first data, an electric appliance identification result corresponding to the first noise signal, where the electric appliance identification model is obtained based on a signal including a second noise signal of at least one known electric appliance.

Activity sensing in the home has a variety of important applications, including healthcare, entertainment, home automation, energy monitoring and post-occupancy research studies. Many existing systems for detecting occupant activity require large numbers of sensors, invasive vision systems, or extensive installation procedures. Disclosed is an approach that uses a single plug-in sensor to detect a variety of electrical events throughout the home. This sensor detects the electrical noise on residential power tines created by the abrupt switching of electrical devices and the noise created by certain devices while in operation. Machine learning techniques are used to recognize electrically noisy events such as turning on or off a particular light switch, a television set, or an electric stove. The system has been tested to evaluate system performance over time and in different types of houses. Results indicate that various electrical events can be learned and classified with accuracies ranging from 85-90%.

The invention relates to a system to identify an electrical device connected to an electrical outlet. The electrical outlet has a sensor configured to measure an electrical signal of an electricity supply to the electrical device connected to the electrical outlet. A processor is configured to receive the measured electrical signal and determine an electrical signature of the electrical device based on one or more features of a frequency domain spectrum of the electrical signal. A processor is configured to compare the electrical signature against a signature database to identify the electrical device.

A smart plug designed to electrically connect a household appliance to power lines. The smart plug has an electronic controller configured to measure current and voltage of the electric power supplied to household appliance via a smart plug, determine electric quantities indicative at least of prefixed current harmonics and/or prefixed voltage harmonics, based on said measured current and voltage, determine load information which are indicative of electric loads of the household appliance being activated during an operating cycle performed by household appliance, based on determined electric quantities; determine the operating cycle performed by the household appliance based on the load information, communicate determined operating cycle to a network system.

Gas meter and center device are provided. Gas meter includes flow rate measurer that measures a flow rate of gas in time series. Center device receives and analyzes flow rate data from gas meter, and monitors states of use of gas appliances. Gas meter detects the start of operation of gas appliances, and transmits flow rate data during predetermined periods before and after the start of operation in accordance with a request from center device. Center device monitors the states of use of gas appliances based on the received flow rate data.

The utility usage of a particular individual occupying a residence may give insight into the individual's current cognitive health and/or to enable provision of various services within the facility for the individual, particularly when monitoring patterns in utility usage over time. To enable accurate and non-invasive utility monitoring, a single-point utility sensor may be secured relative to a utility supply line, and generated signals may be utilized to monitor utility usage and to distinguish between utility fixtures. A centralized computing entity may identify frequency characteristics within the generated data, and may automatically generate one or more machine-learning algorithms to distinguish between utility usage events, without requiring substantial user input.

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.

This disclosure relates generally to method and system for low sampling rate electrical load disaggregation. At low sampling rates, disaggregation of energy load is challenging due to unavailability of events and signatures of the constituent loads. The disclosed energy disaggregation technique receives aggregated load data from a utility meter and sequentially obtains training data for determining disaggregated energy load at low sampling rate. Dictionaries are used to characterize the different loads in terms of power values and time of operation. The obtained dictionary coefficients are treated as graph signals and graph smoothness is used for propagating the coefficients from the training phase to the test phase by formulating an optimization model. The derivation of the optimization model identifies the load of interest and estimate their power consumption based on optimization model constraints. This method achieves accuracy greater than 70% for the loads of interest at low sampling rates.