Premises electric vehicle charge detection
A system includes a processor and a non-transitory, computer-readable memory that includes instructions executable by the processor for causing the processor to perform operations. The operations include accessing premises consumption data of a premises in a power distribution network. The premises consumption data includes an indication of premises resource consumption over a period of time. The operations also include applying a machine-learning model to the premises consumption data. The machine-learning model is trained to generate an output corresponding to an electric vehicle classification of the premises. Further, the operations include generating the electric vehicle classification of the premises using the output of the machine-learning model and controlling power generation of the power distribution network based on the electric vehicle classification of the premises.
1 . A system comprising:
a processor; and
a non-transitory, computer-readable memory that includes instructions executable by the processor for causing the processor to perform operations comprising:
accessing premises consumption data of a premises in a power distribution network, wherein the premises consumption data comprises an indication of premises resource consumption over a period of time;
applying a machine-learning model to the premises consumption data, wherein the machine-learning model is trained to generate an output corresponding to an electric vehicle charger classification of the premises;
generating the electric vehicle charger classification of the premises using the output of the machine-learning model; and
controlling power generation of the power distribution network based on the electric vehicle charger classification of the premises.
2 . The system of claim 1 , wherein the operations further comprise:
training the machine-learning model to generate the output corresponding to the electric vehicle charger classification of the premises using training vectors of ground-truth data of a plurality of premises of an additional power distribution network.
3 . The system of claim 2 , wherein the operations further comprise:
updating the machine-learning model using additional consumption data from a plurality of premises of the power distribution network.
4 . The system of claim 1 , wherein the electric vehicle charger classification of the premises comprises an indication that the premises does not charge an electric vehicle, an indication that the premises charges the electric vehicle using a first type of electric vehicle charger, or an indication that the premises charges the electric vehicle using a second type of electric vehicle charger.
5 . The system of claim 1 , wherein the electric vehicle charger classification of the premises comprises a one-hot encoding of a SoftMax function of a set of possible electric vehicle charger classifications.
6 . The system of claim 1 , wherein the indication of premises resource consumption over the period of time comprises a time series of power consumption by the premises over a plurality of days at regular time intervals.
7 . The system of claim 1 , wherein the operations further comprise:
normalizing the premises consumption data using min-max scaling also used to normalize training consumption data used to train the machine-learning model.
8 . The system of claim 1 , wherein the premises consumption data comprises time-domain data.
9 . A non-transitory, computer-readable medium comprising instructions that are executable by a processor for causing the processor to perform operations comprising:
accessing premises consumption data of a premises in a power distribution network, wherein the premises consumption data comprises an indication of premises resource consumption over a period of time;
applying a machine-learning model to the premises consumption data, wherein the machine-learning model is trained to generate an output corresponding to an electric vehicle charger classification of the premises;
generating the electric vehicle charger classification of the premises using the output of the machine-learning model; and
controlling power generation of the power distribution network based on the electric vehicle charger classification of the premises.
10 . The non-transitory, computer-readable medium of claim 9 , wherein the operations further comprise:
training the machine-learning model to generate the output corresponding to the electric vehicle charger classification of the premises using training vectors of ground-truth data of a plurality of premises of an additional power distribution network.
11 . The non-transitory, computer-readable medium of claim 10 , wherein the operations further comprise:
updating the machine-learning model using additional consumption data from a plurality of premises of the power distribution network.
12 . The non-transitory, computer-readable medium of claim 9 , wherein the electric vehicle charger classification of the premises comprises an indication that the premises does not charge an electric vehicle, an indication that the premises charges the electric vehicle using a level 1 electric vehicle charger, or an indication that the premises charges the electric vehicle using a level 2 electric vehicle charger.
13 . The non-transitory, computer-readable medium of claim 9 , wherein the electric vehicle charger classification of the premises comprises a one-hot encoding of a SoftMax function of a set of possible electric vehicle charger classifications.
14 . The non-transitory, computer-readable medium of claim 9 , wherein the operations further include:
applying an additional machine-learning model to the electric vehicle charger classification of the premises; and
generating a forecast of future electric vehicle charging operations using an output of the additional machine-learning model.
15 . A computer-implemented method comprising:
accessing premises consumption data of a premises in a power distribution network, wherein the premises consumption data comprises an indication of premises resource consumption over a period of time;
applying a machine-learning model to the premises consumption data, wherein the machine-learning model is trained to generate an output corresponding to an electric vehicle charger classification of the premises;
generating the electric vehicle charger classification of the premises using the output of the machine-learning model; and
controlling power generation of the power distribution network based on the electric vehicle charger classification of the premises.
16 . The computer-implemented method of claim 15 , further comprising:
applying an additional machine-learning model to the electric vehicle charger classification of the premises and a plurality of additional electric vehicle charger classifications of additional premises in the power distribution network; and
generating a forecast of future electric vehicle charging operations in the power distribution network using an output of the additional machine-learning model.
17 . The computer-implemented method of claim 15 , wherein the electric vehicle charger classification of the premises comprises an indication that the premises does not charge an electric vehicle, an indication that the premises charges the electric vehicle using a level 1 electric vehicle charger, or an indication that the premises charges the electric vehicle using a level 2 electric vehicle charger.
18 . The computer-implemented method of claim 15 , wherein the electric vehicle charger classification of the premises comprises a one-hot encoding of a SoftMax function of a set of possible electric vehicle charger classifications.
19 . The computer-implemented method of claim 15 , wherein the indication of premises resource consumption over the period of time comprises a time series of power consumption by the premises over a plurality of days at regular time intervals.
20 . The computer-implemented method of claim 15 , wherein the premises consumption data comprises time-domain data.