ELECTRICAL VEHICLE DETECTION FROM ELECTRIC INTERVAL DATA
The disclosure describes three complimentary, synergistically interacting, and yet individually capable techniques for detecting electrical vehicle charging activities. In one example, convolutional neural network techniques find “edges” or points of significant change in electricity consumption. Time-series of electricity consumption are examined, and temperature is considered to normalize for changes in heating, ventilation, and air conditioning consumption. In an example, a time-series of electrical-consumption data of a service site is obtained over a time-range. The time-series of electrical-consumption data is converted into a time-series of consumption-change data. Temperature data may be associated with terms of the time-series of consumption-change data to thereby create input data for a machine-learned algorithm over the time-range. The input data is provided to a machine-learned model. The input data is processed over the time-range in the machine-learned model to generate output, such as a likelihood value of at least one EV charging event during the time-range.
1 . A method of detecting electric vehicle (EV) charging, comprising:
obtaining a time-series of electrical-consumption data of a service site over a time-range;
converting the time-series of electrical-consumption data into a time-series of consumption-change data;
associating temperature data with terms of the time-series of consumption-change data to thereby create input data over the time-range;
providing the input data over the time-range to a machine-learned model;
processing the input data over the time-range in the machine-learned model to generate output, wherein the output comprises a likelihood value of at least one EV charging event during the time-range; and
receiving output from the machine-learned model.
2 . The method of claim 1 , wherein processing the input data over the time-range in the machine-learned model comprises:
recognizing a change in consumption between two terms in the time-series of consumption-change data, wherein the change in consumption is greater than a threshold value.
3 . The method of claim 1 , wherein the machine-learned model comprises a convolutional neural network (CNN), and wherein processing the input data over the time-range in the CNN comprises:
determining weights to associate with terms in the time-series of consumption-change data; and
using the weights associated with a plurality of terms to derive the output from the CNN.
4 . The method of claim 1 , wherein the machine-learned model comprises a convolutional neural network (CNN), and wherein the CNN is trained on input comprising:
labeled data based on EV charging events; and
labeled data based on HVAC operational events.
5 . The method of claim 1 , additionally comprising:
determining a change in temperature data coincident with terms in the time-series of consumption-change data indicating an increase in electricity consumption; and
distinguishing the change in consumption from an EV charging event.
6 . The method of claim 1 , additionally comprising, responsive to detection of an EV charging event performing at least one of:
changing a transformer used by the service site;
changing a number of service sites served by the transformer; or
changing a phase of electricity provided to the transformer.
7 . The method of claim 1 , wherein:
the likelihood value comprises a value from 0.0 to 1.0; and
the time-range is one week.
8 . A computing device to detect electric vehicle (EV) charging events, wherein the computing device comprises:
a processor; and
a memory device, in communication with the processor, wherein the memory device comprises statements executed by the processor to perform actions comprising:
obtaining a time-series of electrical-consumption data of a service site over a time-range;
converting the time-series of electrical-consumption data into a time-series of consumption-change data;
associating temperature data with terms of the time-series of consumption-change data to thereby create input data over the time-range;
providing the input data over the time-range to a subroutine configured to detect EV charging incidents, wherein the subroutine performs actions comprising:
identifying load-changes within the time-range, wherein the load-changes are of a magnitude to indicate EV charging; and
filtering the load-changes to remove load-changes coincident with temperature changes; and
responsive to the subroutine recognizing one or more possible EV charging incidents, determining a likelihood of at least one EV charging event during the time-range based at least in part on the one or more possible EV charging events.
9 . The computing device of claim 8 , wherein identifying load-changes within the time-range by the subroutine comprises:
recognizing a change in consumption between two terms in the time-series of consumption-change data, wherein the change in consumption is 3 kWh or more.
10 . The computing device of claim 8 , wherein the subroutine comprises a convolutional neural network (CNN), and wherein the subroutine performs additional actions comprising:
determining weights to associate with terms in the time-series of consumption-change data; and
using the weights associated with a plurality of terms to derive an output from the CNN.
11 . The computing device of claim 8 , wherein the subroutine comprises a convolutional neural network (CNN), and wherein the CNN is trained on input comprising:
labeled data based on EV charging events; and
labeled data based on HVAC operational events.
12 . The computing device of claim 8 , additionally comprising:
determining a change in temperature data coincident with terms in the time-series of consumption-change data indicating an increase in electricity consumption; and
distinguishing the change in consumption from an EV charging event.
13 . The computing device of claim 8 , based at least in part on the likelihood of the at least one EV charging event, performing at least one of:
changing a transformer used by the service site; or
changing a number of service sites served by the transformer.
14 . The computing device of claim 8 , wherein:
the likelihood comprises a value from 0.0 to 1.0; and
the time-range is one week.
15 . One or more non-transitory computer-readable media storing computer-executable instructions that, when executed by one or more processors, configure a computing device to perform actions comprising:
obtaining a time-series of electrical-consumption data of a service site over a time-range;
converting the time-series of electrical-consumption data into a time-series of consumption-change data;
associating temperature data with terms of the time-series of consumption-change data to thereby create input data over the time-range;
providing the input data over the time-range to a machine-learned model;
processing the input data over the time-range in the machine-learned model to generate output, wherein the output comprises a likelihood value of at least one EV charging event during the time-range; and
receiving output from the machine-learned model.
16 . One or more computer-readable media of claim 15 , wherein processing the input data over the time-range in the machine-learned model comprises:
recognizing a change in consumption between two terms in the time-series of consumption-change data, wherein the change in consumption is greater than a threshold value.
17 . One or more computer-readable media of claim 15 , wherein the machine-learned model comprises a convolutional neural network (CNN), and wherein processing the input data over the time-range in the CNN comprises:
determining weights to associate with terms in the time-series of consumption-change data; and
using the weights associated with a plurality of terms to derive the output.
18 . One or more computer-readable media of claim 15 , wherein the machine-learned model comprises a convolutional neural network (CNN), and wherein the CNN is trained on input comprising:
labeled data based on EV charging events; and
labeled data based on HVAC operational events.
19 . One or more computer-readable media of claim 15 , additionally comprising:
determining a change in temperature data coincident with terms in the time-series of consumption-change data indicating an increase in electricity consumption; and
distinguishing the change in consumption from an EV charging event.
20 . One or more computer-readable media of claim 15 , additionally comprising, responsive to detection of an EV charging event performing at least one of:
changing a transformer used by the service site; or
changing a number of service sites served by the transformer.