Method for quantile probabilistic short-term power load ensemble forecasting, electronic device and storage medium
US11488074B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Dec 11, 2020 |
| Grant date | Nov 1, 2022 |
| Priority date | — |
| Expiry date | Jan 12, 2041 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY04S10/50
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The disclosure relates to a quantile probabilistic short-term power load ensemble forecasting method. The method includes: dividing historical power load data of a power system into a first data set and a second data set; performing bootstrap sampling on the first data set to generate multiple training data sets; training a neural network quantile regression model, a random forest quantile regression model and a gradient boosting regression tree regression model for the each training data set to obtain quantile forecasting models; establishing an optimization model with an objective function for minimizing the quantile loss for the second data set, and determining a weight for each of the quantile regression models, to calculate a power load ensemble forecasting model for predicting the power load in the power system.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.