Method for construction of long-term prediction intervals and its structural learning for gaseous system in steel industry
US11526789B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 12, 2018 |
| Grant date | Dec 13, 2022 |
| Priority date | — |
| Expiry date | Jun 30, 2040 |
Classification
- Technology area (CPC G)Physics
- CPC primaryG06F2218/00
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
The present invention belongs to the field of information technology, involving the techniques of fuzzy modeling, reinforcement learning, parallel computing, etc. It is a method combining granular computing and reinforcement learning for construction of long-term prediction interval and determination of its structure. Adopting real industrial data, the present invention constructs multi-layer structure for assigning information granularity in unequal length and establishes corresponding optimization model at first. Then considering the importance of the structure on prediction accuracy, Monte-Carlo method is deployed to learn the structural parameters. Based on the optimal multi-layer granular computing structure along with implementing parallel computing strategy, the long-term prediction intervals of gaseous generation and consumption are finally obtained. The proposed method exhibits superiority on accuracy and computing efficiency which satisfies the demand of real-world application. It can be also generalized to apply on other energy systems in steel industry.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.