AI-Powered Digital Twin for the Power System
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The UK is committed to achieving Net Zero emissions by 2050 (BEIS, 2019). Achieving this requires extensive reconfiguration of our energy system from 'top-down' to 'bottom-up', integrating new low-carbon technologies (e.g. generation sources, electric vehicle charge points and heat pumps) and new energy services. One of the greatest challenges in achieving this transformation is poor visibility of the loads and capacity of the electricity grid, particularly in the last mile where a significant proportion of technologies need to be deployed (ESC, 2020).Farad AI (FAI) has developed an AI-powered digital-twin of the UK's electricity grid, providing stakeholders with significantly better visibility of energy demand and network constraints. The platform has integrated machine learning algorithms that model local energy demand to predict substation constraints.
Our Phase II project builds on the work achieved in Phase I to improve our regional coverage, integrate new data sets and increase the accuracy of our model predictions. The project activities include significant additional user research / testing, front- and back-end development, DevOps and commercial exploitation.
| Ali Safari | PM_PER |
Subjects by relevance
- Emissions
- Energy technology
- Development (active)
- Renewable energy sources
- Machine learning
- Energy
- Climate changes
Extracted key phrases
- AI
- New energy service
- Local energy demand
- Power System
- Digital Twin
- Net Zero emission
- Energy system
- New data set
- New low
- Electricity grid
- Significant additional user research
- Electric vehicle charge point
- UK
- Carbon technology
- Phase II project