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.

Techniques determine if a gas service (e.g., piping and/or meter) is undersized for the customer's needs. In one example, flowrate information corresponding to gas usage at a service site over a first period of time is obtained. The flowrate information is disaggregated to determine an expected flowrate associated with each of two or more appliances having generally fixed-rates of gas consumption. Flowrate information is again obtained, corresponding to a second period of time. The second flowrate information is compared to one or more combinations (i.e., summations) of the expected flowrates associated with each of the two or more appliances. Based on the comparison, it may be determined that the service site is not appropriately sized. In an example, failure to detect two fixed-rate of gas-consumption appliances operating at their respective fixed-rates at the same time may indicate that the service cannot provide gas at a sufficient flowrate.

The systems and methods described herein are directed to resource monitoring and resource consumption analytics. Resource usage is tracked through a gateway device monitoring resources using remote input sensors, and usage data is transmitted to a central processing unit whereby the data is interpreted and compared with usage over time and site conditions such as weather. For example, incoming usage data is compared with resource signatures in a signature library representing an ideal usage or historical usage for given site condition. This data is interpreted into simple command displays with alerts, alarms, thereby reporting and alerting to an end-user via multiple delivery mechanisms, of potential sources of resource waste or loss. Further, the alerts or alarms can include easily interpreted recommendations to allow a non-skilled worker to take corrective procedures to maximize efficient use of the consumable resources.

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 system is provided for tracking, in a distributed water infrastructure, water usage by category. The system may comprise at least one processor configured to receive from at least one sensor associated with the distributed water infrastructure signals indicative of water usage in the distributed water infrastructure. The system may, based on the signals indicative of water usage, construct a plurality of profiles. The system may assign each profile to one of a plurality of water usage categories. The system may collect, from the at least one sensor, signals indicative of water usage for substantially all water delivered through the distributed water infrastructure in a time period. The system may construct a plurality of water usage profiles in the aggregate, encompassing substantially all water delivered through the distributed water infrastructure in the time period. The system may assign each constructed water usage profile to one of the plurality of water usage categories. The system may output, for display, water usage for the time period for each of the plurality of water usage categories.

The systems and methods described herein are directed to resource monitoring and resource consumption analytics. Resource usage is tracked through a gateway device monitoring resources using remote input sensors, and usage data is transmitted to a central processing unit whereby the data is interpreted and compared with usage over time and site conditions such as weather. For example, incoming usage data is compared with resource signatures in a signature library representing an ideal usage or historical usage for given site condition. This data is interpreted into simple command displays with alerts, alarms, thereby reporting and alerting to an end-user via multiple delivery mechanisms, of potential sources of resource waste or loss. Further, the alerts or alarms can include easily interpreted recommendations to allow a non-skilled worker to take corrective procedures to maximize efficient use of the consumable resources.

A system is provided for tracking, in a distributed water infrastructure, water usage by category. The system may comprise at least one processor configured to receive from at least one sensor associated with the distributed water infrastructure signals indicative of water usage in the distributed water infrastructure. The system may, based on the signals indicative of water usage, construct a plurality of profiles. The system may assign each profile to one of a plurality of water usage categories. The system may collect, from the at least one sensor, signals indicative of water usage for substantially all water delivered through the distributed water infrastructure in a time period. The system may construct a plurality of water usage profiles in the aggregate, encompassing substantially all water delivered through the distributed water infrastructure in the time period. The system may assign each constructed water usage profile to one of the plurality of water usage categories. The system may output, for display, water usage for the time period for each of the plurality of water usage categories.

Techniques determine if a gas service (e.g., piping and/or meter) is under-sized for the customer's needs. In one example, flowrate information corresponding to gas usage at a service site over a first period of time is obtained. The flowrate information is disaggregated to determine an expected flowrate associated with each of two or more appliances having generally fixed-rates of gas consumption. Flowrate information is again obtained, corresponding to a second period of time. The second flowrate information is compared to one or more combinations (i.e., summations) of the expected flowrates associated with each of the two or more appliances. Based on the comparison, it may be determined that the service site is not appropriately sized. In an example, failure to detect two fixed-rate of gas-consumption appliances operating at their respective fixed-rates at the same time may indicate that the service cannot provide gas at a sufficient flowrate.

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.

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.