公开(公告)号：US20240421631A1

公开(公告)日：2024-12-19

申请号：US18209419

申请日：2023-06-13

Applicant: Itron, Inc.

Inventor： Caelum Rodriguez ,  Yingjuan Du

IPC: H02J13/00 ,  H02J3/00

Abstract: 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.