公开(公告)号：US10310459B2

公开(公告)日：2019-06-04

申请号：US15715692

申请日：2017-09-26

Applicant: Oracle International Corporation

Inventor： Kenny C. Gross ,  Mengying Li ,  Benjamin P. Franklin, Jr.

IPC: G05D3/12 ,  G05B13/04 ,  H02J3/00 ,  G05B13/02

Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, wherein each smart meter gathers electrical usage data from a customer of the utility system. Next, the system uses the set of input signals to train an inferential model, which learns correlations among the set of input signals, and uses the inferential model to produce a set of inferential signals, wherein an inferential signal is produced for each input signal in the set of input signals. The system then uses a Fourier-based technique to decompose each inferential signal into deterministic and stochastic components, and uses the deterministic and stochastic components to generate a set of synthesized signals, which are statistically indistinguishable from the inferential signals. Finally, the system projects the set of synthesized signals into the future to produce a forecast for the electricity demand.

公开(公告)号：US10937114B2

公开(公告)日：2021-03-02

申请号：US16355127

申请日：2019-03-15

Applicant: Oracle International Corporation

Inventor： Benjamin P. Franklin, Jr. ,  Kenny C. Gross ,  Cornell Thomas Eyford, III ,  Bradley R. Williams

IPC: G06Q50/06 ,  G06Q10/06 ,  G06Q30/02

Abstract: During operation, the system receives a set of input signals containing electrical usage data from a set of smart meters, which gather electrical usage data from customers of the utility system. The system uses the set of input signals and a projection technique to produce projected loadshapes, which are associated with electricity usage in the utility system. Next, the system identifies a closest time period in a database containing recent empirically obtained load-related parameters for the utility system, wherein the load-related parameters in the closest time period are closest to a present set of load-related parameters for the utility system. The system then iteratively adjusts the projected loadshapes based on changes indicated by the load-related parameters in the closest time period until a magnitude of adjustments falls below a threshold. Finally, the system predicts electricity demand for the utility system based on the projected loadshapes.