PEESE research on quantum computing and AI for smart energy systems was reported by some news media. Some recent ones include, but are not limited to, the ones below:
- Cornell Chronicle: Tying quantum computing to AI prompts a smarter power grid
- Tech Xplore and phys.org: Tying quantum computing to AI prompts a smarter power grid
- HPCwire: Cornell Researchers Integrate Quantum Computing with AI for a Smarter Power Grid
- Oak Ridge National Laboratory News: A New Design for Quantum Computer–Monitored Electrical Grids
- Communications of the ACM: Tying Quantum Computing to AI Prompts Smarter Power Grid
- Smart Energy: Quantum computing shows potential for power system fault analysis
- Mirage News: Tying quantum computing to AI prompts smarter power grid
- RoxxCloud: Linking quantum computing to AI leads to a smarter electricity grid
- ElectronicsForu: Combining Quantum Computing And AI For Smart Power Grids
- Qianzhan, Baidu, and 163
These are for our recent paper titled “Quantum Computing-based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems.”, as well as an earlier paper on quantum computing-based deep learning for fault detection and diagnosis in industrial manufacturing. A patent was filed for this invention.
Relevant papers from PEESE grad student Akshay Ajagekar are listed below:
- Ajagekar, A., & You, F.* (2021). Quantum Computing based Hybrid Deep Learning for Fault Diagnosis in Electrical Power Systems. Applied Energy, 303, 117628.
- Ajagekar, A., & You, F.* (2020). Quantum Computing Assisted Deep Learning for Fault Detection and Diagnosis in Industrial Process Systems. Computers & Chemical Engineering, 143, 107119.
- Ajagekar, A., Humble, T., & You, F.* (2020). Quantum Computing based Hybrid Solution Strategies for Large-scale Discrete-Continuous Optimization Problems. Computers & Chemical Engineering, 132, 106630.
- Ajagekar, A., & You, F.* (2019). Quantum computing for energy systems optimization: Challenges and opportunities. Energy, 179, 76-89.