Explainable AI for Energy Applications
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Explainable AI for Energy Applications
CoPED ID
d6f74db4-d80e-45b8-b478-f977f37cec6c
Status
Active
Value
No funds listed.
Start Date
Sept. 26, 2021
End Date
Sept. 25, 2025
Description
This PhD, will explore and develop Explainable AI (XAI) for energy systems. These methods will be able to provide insights into how the 'black-box' methods are functioning, approaching glass-box methods. Methods explored include but are not limited to; deep learning, ensemble learning and causal inference. The research contribution will cover a wide spectrum building theoretical foundations in this rapidly developing field while also contributing to real world applications such as wind turbine failure. The research will be conducted in collaboration with the EDF digital innovation team.
| University College London | LEAD_ORG |
| EDF Energy Plc | STUDENT_PP_ORG |
| Aidan O'Sullivan | SUPER_PER |
| Antoine Pesenti | STUDENT_PER |
Subjects by relevance
- Machine learning
- Research methods
- Research
- Neural networks (information technology)
Extracted key phrases
- Explainable AI
- Box method
- Wide spectrum building theoretical foundation
- Energy system
- Energy Applications
- Ensemble learning
- Deep learning
- Research contribution
- Real world application
- Wind turbine failure
- XAI
- Phd