Techniques determine if an appliance having a fixed-rate of gas-consumption is degrading over time. In one example, a flowrate of gas at a service site is obtained. The flowrate of gas is disaggregated to obtain a flowrate of gas corresponding to an appliance having a generally fixed-rate of gas-consumption. The flowrate of gas of the appliance is compared to historical gas consumption by the appliance. Based at least in part on the comparing, it may be determined that performance of the appliance has changed over time. For example, the gas consumption of a hot water tank may increase due to mineral build-up in the bottom of the tank. Responsive to the determined degradation of the appliance, warnings may be sent, repairs may be made, and/or appliance(s) may be replaced.

A method for operating a power consumption metering system and a power consumption metering system are disclosed. In an embodiment a method include measuring, by a sensor deployed at a monitored site, high speed power consumption values over time to obtain a high speed value pattern of power consumption with a resolution of more than 1000 values per second, determining one or more harmonics of the high speed value pattern, measuring, by the sensor, low speed power consumption values over time to obtain a low speed value pattern of the power consumption with a resolution of less than 100 values per second, providing the harmonics and the low speed value pattern to a cloud based data processing system and identifying a status of a power consumer of the monitored site dependent on the measured harmonics and the low speed value pattern.

An energy meter is configured to determine component waveforms that form a measured waveform. The meter inputs the waveform into one or more entries of a data structure, each entry of the one or more entries of the data structure storing a weight value that is determined based at least in part on values of the data signatures representing the plurality of remote devices, each entry being connected to one or more other entries of the data structure. The meter, for each of the one or more entries, generates an output value by performing an arithmetic operation on the waveform stored at that entry, the arithmetic operation comprising a function of the weight value. The meter identifies, from among the data signatures, one or more particular data signatures that are represented in the waveform. The meter determines, based on the particular data signatures, an operational state of another device.

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.

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.

Techniques determine if an appliance having a fixed-rate of gas-consumption is degrading over time. In one example, a flowrate of gas at a service site is obtained. The flowrate of gas is disaggregated to obtain a flowrate of gas corresponding to an appliance having a generally fixed-rate of gas-consumption. The flowrate of gas of the appliance is compared to historical gas consumption by the appliance. Based at least in part on the comparing, it may be determined that performance of the appliance has changed over time. For example, the gas consumption of a hot water tank may increase due to mineral build-up in the bottom of the tank. Responsive to the determined degradation of the appliance, warnings may be sent, repairs may be made, and/or appliance(s) may be replaced.

An electronic sensing and allocation system is provided for a distributed water infrastructure containing a plurality of differing appliances. The system may receive, from at least one sensor upstream of the plurality of differing appliances, a plurality of signals indicative of water usage within the distributed water infrastructure. The system may output a first indication of a first volume of water together with an indicator attributing the first volume of water to a first rate schedule, and output a second indication of a second volume of water together with an indicator attributing the second volume of water to a second rate schedule. The system may enable billing of the first and second volumes of water to a consumer at differing rates based on differing uses.

The present invention is generally directed to systems and methods for learning appliance signatures based at least in part upon, energy disaggregation techniques and user input Methods of the present invention may include retrieving energy consumption data pertaining to at least one home environment comprising one or more appliances; identifying one or more patterns in the energy consumption data by applying signal processing algorithms to the consumption data; generating at least one question for a user based at least in part on the one or more patterns; receiving a user input In response to the question; determining at least one appliance in the home environment, based at least in part on the one or more patterns and the user input; and determining an appliance signature by extracting a canonical pattern from the energy consumption data based at least in part on the user input.

An electronic electricity meter (102) for monitoring electrical power consumption due to a plurality of loads, comprising electric power sensor (506A, 506, 502, 504, 508) configured to register, optionally in a substantially real-time fashion, data indicative of aggregate power demand (202) of a number of loads coupled to a common electrical power source, such as one or more phases of a polyphase system, load tracker (506B, 506, 502, 504) configured to detect the effect of individual loads on the basis of distinctive load patterns in said data, wherein the tracker is configured to utilize a distinctive load pattern detected in said data as at least a basis for a reference pattern (304, 306, 308) for subsequent detections (304a, 306a, 308a) of the effect of the same load in the data, accuracy analyzer (506C, 506, 502, 504) configured to, on the basis of comparisons of subsequent detections with the corresponding references, determine (312, 314, 316) whether the comparisons relating to at least two, preferably three, loads each indicate the difference between the subsequently detected pattern and the corresponding reference exceeding a predetermined threshold, and notifier (506D, 506, 502, 504, 508) configured to send, provided that positive determination has taken place (318), a notification signal indicative of potential fault with the electricity meter towards an external entity (106, 108). Corresponding arrangement and method are presented.

An abnormal consumption detection system is provided with remote valve control for a distributed water infrastructure. The system may comprise an electronically controllable valve, a remote communication transmitter, a remote communication receiver, at least one sensor for measuring water flow information associated with the distributed water infrastructure, and at least one processor. The system may determine from the water flow information obtained from the at least one sensor a potential abnormal consumption associated with the distributed water infrastructure. The system may automatically close a valve, without human intervention, when the potential abnormal consumption is determined. The system may transmit, via the remote communication transmitter to a remote administrator, alert information about the potential abnormal consumption to enable an administrator to decide based on the transmitted information whether to reopen the valve. The system may receive from the administrator via the remote communication receiver a control signal to reopen the valve, despite the information about the potential abnormal consumption, and reopen the valve.