Deep reinforcement learning based real-time scheduling of Energy Storage System (ESS) in commercial campus
US11610214B2 · kind B2 · utility
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Key dates
| Filing date | Nov 24, 2020 |
| Grant date | Mar 21, 2023 |
| Priority date | — |
| Expiry date | Dec 6, 2040 |
Classification
- Technology area (CPC Y)Emerging Cross-Sectional Technologies
- CPC primaryY02T10/70
- WIPO fieldComputer technology
- WIPO sectorElectrical engineering
Abstract
A system with deep reinforcement learning based control determines optimal actions for major components in a commercial building to minimize operation costs while maximizing comprehensive comfort levels of occupants. An unsupervised deep Q-network method is introduced to handle the energy management problem by evaluating the influence of operation costs on comfort levels considering the environment factors at each time slot. An optimum control decision can be derived that targets both immediate and long-term goals, where exploration and exploitation are considered simultaneously.
Source: USPTO / EPO open patent data. Objective bibliographic and citation counts.