Applying quantum machine learning to wind, solar, and demand forecasting for grid balancing.
For energy forecasting teams and grid operators. Covers quantum machine learning for wind and solar output prediction, quantum optimisation for grid balancing and storage dispatch, benchmark-specific performance comparisons against classical methods, and honest assessment of NISQ hardware limits for forecasting workloads.
Course content is structured into sessions below. Each session is configurable to your organisation's environment, threat model, and objectives.
| # | Session | Topics |
|---|---|---|
| 1 | Classical Forecasting and Its Computational Boundaries Why numerical weather prediction and statistical methods plateau | |
| 2 | Quantum Machine Learning for Energy Forecasting QML architectures applicable to wind, solar, and demand prediction |
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| Break | ||
| 3 | Grid Balancing and Storage Dispatch Optimisation Quantum approaches to real-time energy system management |
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| 4 | Interactive Demonstration: Quantum Forecasting Pipeline Full-day format only |
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| Break | ||
| 5 | Current Hardware Limits and Honest Performance Assessment What works now, what does not, and the 2026-2030 frontier |
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| 6 | Data Infrastructure and Vendor Landscape What you need before engaging a quantum provider |
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| 7 | Q&A and Pilot Planning | |
Training courses are designed and delivered by QSECDEF in collaboration with subject specialists. All instructors have direct experience in quantum technologies and security.
Course design and delivery
QSECDEF brings expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our membership spans 900+ organisations and 1,500+ professionals working at the intersection of quantum technologies and critical infrastructure security.
Get in touch to discuss scheduling, group pricing, and how this course can be configured to your organisation's needs.
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