# US 2022/0044159 A1 · Electrical meter for training a mathematical model for a device using a smart plug > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2022/0044159 A1 - Document ID: US20220044159A1 - Status: Published application - Kind: A1 - Application number: 17/510,256 - Filed: 2021-10-25 - Published: 2022-02-10 - Earliest claimed: 2018-10-02 - Assignees: Sense Labs, Inc. - Applicants: Sense Labs, Inc. - Inventors: Ghinwa Fakhri Choueiter; Jonah Wyman Petri - Web page: https://patentgrep.com/patent/US20220044159A1 ## CPC - H04L 67/125: involving control of end-device applications over a network - G01R 22/063: {related to remote communication} - G01R 22/10: using digital techniques - G05B 19/042: using digital processors (G05B19/05 takes precedence) - G06F 1/28: Supervision thereof, e.g. detecting power-supply failure by out of limits supervision - G06F 1/30: Means for acting in the event of power-supply failure or interruption, e.g. power-supply fluctuations (for resetting only G06F1/24) - G06F 16/2379: {Updates performed during online database operations; commit processing} - G06N 3/044: Recurrent networks, e.g. Hopfield networks - G06N 3/084: Backpropagation, e.g. using gradient descent - G06N 3/09: Supervised learning - G06N 5/046: Forward inferencing; Production systems - G06N 7/01: Probabilistic graphical models, e.g. probabilistic networks - G06N 20/00: Machine learning - G06N 20/20: Ensemble learning - H02J 13/34: the equipment being switches, relays or circuit breakers - H04L 67/12: specially adapted for proprietary or special-purpose networking environments, e.g. medical networks, sensor networks, networks in vehicles or remote metering networks - H04Q 9/00: Arrangements in telecontrol or telemetry systems for selectively calling a substation from a main station, in which substation desired apparatus is selected for applying a control signal thereto or for obtaining measured values therefrom - G06N 3/088: Non-supervised learning, e.g. competitive learning - G06N 5/01: Dynamic search techniques; Heuristics; Dynamic trees; Branch-and-bound - G06N 20/10: using kernel methods, e.g. support vector machines [SVM] - H02J 13/1311: {using the power network as support for the transmission} - H04Q 2209/84: Measuring functions - Y02E 60/00: Enabling technologies; Technologies with a potential or indirect contribution to GHG emissions mitigation - Y04S 10/18: using switches, relays or circuit breakers, e.g. intelligent electronic devices [IED] - Y04S 40/121: using the power network as support for the transmission - Y04S 40/18: Network protocols supporting networked applications, e.g. including control of end-device applications over a network - H04L: TRANSMISSION OF DIGITAL INFORMATION, e.g. TELEGRAPHIC COMMUNICATION (arrangements common to telegraphic and telephonic communication H04M) - G01R: MEASURING ELECTRIC VARIABLES; MEASURING MAGNETIC VARIABLES (indicating correct tuning of resonant circuits H03J3/12) - G05B: CONTROL OR REGULATING SYSTEMS IN GENERAL; FUNCTIONAL ELEMENTS OF SUCH SYSTEMS; MONITORING OR TESTING ARRANGEMENTS FOR SUCH SYSTEMS OR ELEMENTS (systems for controlling or regulating non-electric variables G05D; systems for regulating electric or magnetic variables G05F; control devices or systems insofar as characterised by mechanical features only G05G) - G06F: ELECTRIC DIGITAL DATA PROCESSING (computer systems based on specific computational models G06N) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - H02J: ELECTRIC POWER NETWORKS; CIRCUIT ARRANGEMENTS OR SYSTEMS FOR SUPPLYING OR DISTRIBUTING ELECTRIC POWER; SYSTEMS FOR STORING ELECTRIC ENERGY - H04Q: SELECTING (switches, relays, selectors H01H; wireless communication networks H04W) - Y02E: REDUCTION OF GREENHOUSE GAS [GHG] EMISSIONS, RELATED TO ENERGY GENERATION, TRANSMISSION OR DISTRIBUTION - Y04S: SYSTEMS INTEGRATING TECHNOLOGIES RELATED TO POWER NETWORK OPERATION, COMMUNICATION OR INFORMATION TECHNOLOGIES FOR IMPROVING THE ELECTRICAL POWER GENERATION, TRANSMISSION, DISTRIBUTION, MANAGEMENT OR USAGE, i.e. SMART GRIDS ## Abstract An electrical panel or an electrical meter may provide improved functionality by interacting with a smart plug. A smart plug may provide a smart-plug power monitoring signal that includes information about power consumption of devices connected to the smart plug. The smart-plug power monitoring signal may be used in conjunction with power monitoring signals from the electrical mains of the building for providing information about the operation of devices in the building. For example, the power monitoring signals may be used to (i) determine the main of the house that provides power to the smart plug, (ii) identify devices receiving power from the smart plug, (iii) improve the accuracy of identifying device state changes, and (iv) train mathematical models for identifying devices and device state changes. ## Claims ### Claim 1 An electrical meter comprising: a sensor for measuring an electrical property of a first electrical main, wherein the first electrical main provides power to devices in a building; a network interface; and at least one processor configured to: receive a smart-plug power monitoring signal, wherein the smart-plug power monitoring signal indicates an amount of power provided by a smart plug to one or more devices, the one or more devices comprising a first device; identify a plurality of turn-on times corresponding to the smart-plug power monitoring signal transitioning from zero power to non-zero power; identify a plurality of turn-off times corresponding to the smart-plug power monitoring signal transitioning from non-zero power to zero power; obtain a first-main power monitoring signal using measurements from the sensor; identify power events in the first-main power monitoring signal, wherein each power event corresponds to an event time; cluster the power events into a plurality of clusters; select a first cluster of the plurality of clusters using (i) the plurality of turn-on times and (ii) event times of power events of the first cluster; select a second cluster of the plurality of clusters using (i) the plurality of turn-off times and (ii) event times of power events of the second cluster; train a first transition model for the first device using one or more of the power events of the first cluster; and train a second transition model for the first device using one or more of the power events of the second cluster. ### Claim 2 (depends on 1) The electrical meter of claim 1, wherein the at least one processor is configured to select the first cluster by determining, for each event time of the power events of the first cluster, a closest turn-on time. ### Claim 3 (depends on 1) The electrical meter of claim 1, wherein the at least one processor is configured to: create a state model for the first device comprising two states; compute a wattage model for the first device; compute a score indicating a goodness of fit of the wattage model for the first device to the smart-plug power monitoring signal; and determine to add a third state to the state model for the first device using the score. ### Claim 4 (depends on 3) The electrical meter of claim 3, wherein the at least one processor is configured to select a third cluster of the plurality of clusters using (i) the plurality of turn-on times, (ii) the plurality of turn-off times, and (iii) event times of the power events of the third cluster. ### Claim 5 (depends on 4) The electrical meter of claim 4, wherein the at least one processor is configured to determine, for each event time of the power events of the third cluster, whether the event time is between a turn-on time and a subsequent turn-off time. ### Claim 6 (depends on 3) The electrical meter of claim 3, wherein the at least one processor is configured to compute the wattage model for the first device by computing an expected amount of power consumed by the first device from the first-main power monitoring signal. ### Claim 7 (depends on 6) The electrical meter of claim 6, wherein the expected amount of power is a mean or median of power in the first-main power monitoring signal after the power events of the first cluster. ### Claim 8 (depends on 6) The electrical meter of claim 6, wherein the expected amount of power is a mean or median of power in the smart-plug power monitoring signal after the turn-on times. ### Claim 9 A system for training a mathematical model for a first device, the system comprising: an electrical meter comprising at least one processor and at least one memory, the electrical meter configured to: receive a smart-plug power monitoring signal, wherein the smart-plug power monitoring signal indicates an amount of power provided by a smart plug to one or more devices, the one or more devices comprising the first device; identify a plurality of turn-on times corresponding to the smart-plug power monitoring signal transitioning from zero power to non-zero power; identify a plurality of turn-off times corresponding to the smart-plug power monitoring signal transitioning from non-zero power to zero power; obtain a first-main power monitoring signal using measurements from a sensor that measures an electrical property of a first electrical main of a building; identify power events in the first-main power monitoring signal, wherein each power event corresponds to an event time; cluster the power events into a plurality of clusters; select a first cluster of the plurality of clusters using (i) the plurality of turn-on times and (ii) event times of power events of the first cluster; select a second cluster of the plurality of clusters using (i) the plurality of turn-off times and (ii) event times of power events of the second cluster; train a first transition model for the first device using one or more of the power events of the first cluster; and train a second transition model for the first device using one or more of the power events of the second cluster. ### Claim 10 (depends on 9) The system of claim 9, wherein the electrical meter is configured to transmit the first transition model and the second transition model to a server for deployment to a second building. ### Claim 11 (depends on 9) The system of claim 9, wherein the electrical meter is configured to select the first cluster by determining, for each event time of the power events of the first cluster, a closest turn-on time. ### Claim 12 (depends on 9) The system of claim 9, wherein the electrical meter is configured to: create a state model for the first device comprising two states; compute a wattage model for the first device; compute a score indicating a goodness of fit of the wattage model for the first device to the smart-plug power monitoring signal; and determine to add a third state to the state model for the first device using the score. ### Claim 13 (depends on 12) The system of claim 12, wherein the electrical meter is configured to determine to add a fourth state to the state model for the first device. ### Claim 14 (depends on 12) The system of claim 12, wherein the electrical meter is configured to compute the score indicating the goodness of fit of the wattage model by computing a distance between the wattage model and a plurality of portions of the smart-plug power monitoring signal. ### Claim 15 (depends on 9) The system of claim 9, wherein the electrical meter is configured to: compute features for each power event of the power events; and cluster the power events using the features. ### Claim 16 (depends on 15) The system of claim 15, wherein the features computed for a power event comprise one or more of a match between the power event and a template, Fourier coefficients, neural network features, or a change in power, current, voltage, or phase before and after the power event. ### Claim 17 A computer-implemented method, implemented by an electrical meter, for training a mathematical model for a first device, the computer-implemented method comprising: receiving a smart-plug power monitoring signal, wherein the smart-plug power monitoring signal indicates an amount of power provided by a smart plug to one or more devices, the one or more devices comprising the first device; identifying a plurality of turn-on times corresponding to the smart-plug power monitoring signal transitioning from zero power to non-zero power; identifying a plurality of turn-off times corresponding to the smart-plug power monitoring signal transitioning from non-zero power to zero power; obtaining a first-main power monitoring signal using measurements from a sensor that measures an electrical property of a first electrical main of a building; identifying power events in the first-main power monitoring signal, wherein each power event corresponds to an event time; clustering the power events into a plurality of clusters; selecting a first cluster of the plurality of clusters using (i) the plurality of turn-on times and (ii) event times of power events of the first cluster; selecting a second cluster of the plurality of clusters using (i) the plurality of turn-off times and (ii) event times of power events of the second cluster; training a first transition model for the first device using one or more of the power events of the first cluster; and training a second transition model for the first device using one or more of the power events of the second cluster. ### Claim 18 (depends on 17) The computer-implemented method of claim 17, wherein selecting the first cluster comprises determining, for each event time of the power events of the first cluster, a closest turn-on time. ### Claim 19 (depends on 17) The computer-implemented method of claim 17, wherein clustering the power events comprises using hierarchical clustering, centroid-based clustering, or density-based clustering. ### Claim 20 (depends on 17) The computer-implemented method of claim 17, comprising: computing features for each power event of the power events; wherein clustering the power events comprises clustering the power events using the features.