US 11,893,487 B2
Trained models for discovering target device presence
Selim Mimaroglu, Arlington, VA (US); Oren Benjamin, Arlington, VA (US); Arhan Gunel, Arlington, VA (US); Anqi Shen, Arlington, VA (US); and Ziran Feng, Arlington, VA (US)
Assigned to Oracle International Corporation, Redwood Shores, CA (US)
Filed by Oracle International Corporation, Redwood Shores, CA (US)
Filed on Jun. 23, 2021, as Appl. No. 17/355,780.
Prior Publication US 2022/0414446 A1, Dec. 29, 2022
Int. Cl. G06N 3/08 (2023.01); B60L 53/60 (2019.01); G06N 3/045 (2023.01); G06N 3/044 (2023.01); G06Q 50/06 (2012.01); G06Q 30/0202 (2023.01)
CPC G06N 3/08 (2013.01) [B60L 53/60 (2019.02); G06N 3/044 (2023.01); G06N 3/045 (2023.01); G06Q 50/06 (2013.01); G06Q 30/0202 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A method for generating machine learning predictions to discover target device energy usage, the method comprising:
storing one or more trained machine learning models configured to discover target device energy usage from source location energy usage;
receiving, for a given source location, multiple instances of source location energy usage over a period of time, wherein the multiple instances correspond to windows of time that span the period of time;
generating, using the trained machine learning model, multiple discovery predictions for the received instances of source location energy usage, each of the discovery predictions comprising a prediction about a presence of target device energy usage within the instances of source location energy usage over each of the windows of time; and
generating, by combining the multiple discovery predictions, an overall prediction about a presence of target device energy usage within the given source location's energy usage over the period of time, wherein the overall prediction is used to perform energy grid demand planning, the energy grid being controlled at least in part based on the demand planning.