System and methods for power system forecasting using deep neural networks
US11605036B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Aug 9, 2018 |
| Grant date | Mar 14, 2023 |
| Priority date | — |
| Expiry date | Jan 13, 2042 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S20/222
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A method of managing energy by use of processing logic that comprises a load processor as a cloud service is provided. The method includes receiving power load information from a data collection system located at a building and using a cloud analysis layer that employs machine-learning and artificial intelligence for optimization control, analyzing the received power load information to disaggregate load waveform signals and identify device-based power loads by use of a neural network to perform historical device demand and performance analysis to generate device-based demand forecasting, generating demand forecasts for the building to mitigate peak demand based on analysis of a power draw signal and the generated device-based demand forecasting, and determining whether the generated demand forecast for the building is to peak in a near future, based on threshold values of at least one of generated device-based demand forecasting, power price or cost information, and user behavior analysis.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.