Optimal control with deep learning
US12423572B2 · kind B2 · utility
Assignee
Inventors
Key dates
| Filing date | Sep 8, 2020 |
| Grant date | Sep 23, 2025 |
| Priority date | — |
| Expiry date | Nov 13, 2042 |
Classification
- Technology area (CPC F)Mechanical Engineering; Lighting; Heating
- CPC primaryF24F11/64
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
Embodiments provide a machine learning model to identify changes in setpoints of one or more environmental control modules that, while having a high critical error, provide greater reductions in a cost function associated with the one or more environmental modules provided in an environmentally controlled space. The high critical error associated with the identified changes may still be within an acceptable threshold range associated with the environmentally controlled space. Thus, contrary to rule-based methods, the artificial intelligence (AI) based model described herein may recommended optimal changes to the system that yield to greater savings in the cost function and may not focus on minimizing the critical control error. Rather, the AI-based technique may simply aim at keeping the critical control error within an acceptable threshold range.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.