Method, system and storage medium for predicting power load probability density based on deep learning
US11409347B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Feb 25, 2019 |
| Grant date | Aug 9, 2022 |
| Priority date | — |
| Expiry date | Jun 1, 2041 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06N20/10
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosure provides a method, a system and a storage medium for predicting power load probability density based on deep learning. The method comprises: S101, collecting power load data of a user, meteorological data and air quality data in a preset historical time period, and dividing the collected data into a training set and a test set; S102, determining a deep learning model for predicting power load; S103, inputting the test set into the deep learning model for predicting power load, and obtaining power load prediction data of the user at different quantile points in a third time interval; S104, performing kernel density estimation and obtaining a probability density curve of the power load of the user in the third time interval.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.