Patent · US Active

Solar irradiation prediction using deep learning with end-to-end training

US11900247B2 · kind B2 · utility

0Cited by

1References

12Claims

0Family size

Assignee

Siemens Aktiengesellschaft · DE

Inventors

Ti-chiun Chang · Plainsboro, US
Patrick Reeb · Höchstadt an der Aisch, DE
Joachim Bamberger · Stockdorf, DE
Kuan-Chuan Peng · Plainsboro, US
Jan Ernst · Plainsboro, US

Key dates

Filing date	May 31, 2018
Grant date	Feb 13, 2024
Priority date	—
Expiry date	Dec 26, 2039

Classification

Technology area (CPC G)Physics
CPC primaryG06N3/096
WIPO fieldComputer technology
WIPO sectorElectrical engineering

Abstract

Deep learning is used to train a neural network for end-to-end prediction of short term (e.g., 20 minutes or less) solar irradiation based on camera images and metadata. The architecture of the neural network includes a recurrent network for temporal considerations. The images and metadata are input at different locations in the neural network. The resulting machine-learned neural network predicts solar irradiation based on camera images and metadata so that a solar plant and back-up power source may be controlled to minimize output power variation.

Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.