Training a mathematical model for a device using 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.
1. A computer-implemented method for training a mathematical model for a first device, the 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;
training a second transition model for the first device using one or more of the power events of the second cluster; and
identifying state changes of devices using the first transition model and the second transition model.
2. The computer-implemented method of claim 1 , wherein selecting the first cluster comprises determining, for each event time of the power events of the first cluster, a closest turn-on time.
3. The computer-implemented method of claim 1 , comprising:
creating a state model for the first device comprising two states;
computing a wattage model for the first device;
computing a score indicating a goodness of fit of the wattage model for the first device to the smart-plug power monitoring signal; and
determining to add a third state to the state model for the first device using the score.
4. The computer-implemented method of claim 3 , comprising selecting 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.
5. The computer-implemented method of claim 4 , comprising determining, 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.
6. The computer-implemented method of claim 3 , wherein computing a wattage model for the first device comprises computing an expected amount of power consumed by the first device from the first-main power monitoring signal.
7. The computer-implemented method 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.
8. The computer-implemented method 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.
9. A system for training a mathematical model for a first device, the system comprising:
at least one computer comprising at least one processor and at least one memory, the at least one computer 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;
train a second transition model for the first device using one or more of the power events of the second cluster; and
identify state changes of devices using the first transition model and the second transition model.
10. The system of claim 9 , wherein the at least one computer is configured to deploy the first transition model and the second transition model to a second building.
11. The system of claim 9 , wherein the at least one computer 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.
12. The system of claim 9 , wherein the at least one computer 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.
13. The system of claim 12 , wherein the at least one computer is configured to determine to add a fourth state to the state model for the first device.
14. The system of claim 12 , wherein the at least one computer 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.
15. The system of claim 9 , wherein the at least one computer is configured to:
compute features for each power event of the power events; and
wherein the at least one computer is configured to cluster the power events using the features.
16. 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.
17. One or more non-transitory, computer-readable media comprising computer executable instructions that, when executed, cause at least one processor to perform actions 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 a 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;
training a second transition model for the first device using one or more of the power events of the second cluster; and
identifying state changes of devices using the first transition model and the second transition model.
18. The computer-readable media 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.
19. The computer-readable media of claim 17 , wherein clustering the power events comprises using hierarchical clustering, centroid-based clustering, or density-based clustering.
20. The computer-readable media of claim 17 , wherein the actions comprise:
computing features for each power event of the power events; and
wherein clustering the power events comprises clustering the power events using the features.