Workshops Power & Energy Renewable Energy Forecasting

Power & Energy Full Day or Half Day Workshop

Quantum Computing for Renewable Energy Forecasting

This workshop equips energy forecasting teams and grid operators with a practical assessment of quantum machine learning for wind and solar prediction, grid balancing, and storage dispatch decisions.

Full day (6 hours) or half day

In person or online

Max 30 delegates

Commission This Workshop View Agenda

Proud to recommend our expert members

Workshop Description

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.

Wind and solar output forecasting is fundamentally a high-dimensional time-series problem. Classical approaches (numerical weather prediction, ARIMA, LSTM networks) perform well for 1-6 hour horizons but degrade as the number of correlated input features (wind speed, temperature, humidity, pressure, cloud cover across multiple grid points) grows. Quantum machine learning offers a structurally different approach: variational quantum circuits and quantum kernel methods can represent correlations in high-dimensional feature spaces that are expensive to capture classically. Published results from IBM, Pasqal, and several academic groups show that on specific meteorological datasets, QML models match or slightly exceed classical baselines at 10-40 qubit scales. The open question is whether this advantage persists as problem size grows, and whether the data infrastructure typical of energy companies can support quantum-enhanced pipelines. This workshop maps that boundary for your forecasting portfolio.

What participants cover

Classical forecasting limits: where NWP, ARIMA, and LSTM networks plateau on high-dimensional renewable energy prediction problems
Quantum machine learning architectures: VQCs, quantum kernel estimation (QKE), and quantum reservoir computing applied to meteorological time-series data
Grid balancing optimisation: QAOA and quantum annealing for unit commitment, economic dispatch, and battery storage scheduling under renewable intermittency
Benchmark evidence: published results comparing QML against classical ML on energy forecasting datasets at current NISQ hardware scales
Hardware limits: the NISQ performance ceiling for forecasting (10-40 qubit feature maps with noise mitigation) and fault-tolerant timeline for utility-scale problems
Vendor assessment: independent comparison of IBM Qiskit, Pasqal, Xanadu PennyLane, D-Wave, and quantum-inspired classical alternatives for energy workloads

Preliminary Agenda

Full-day session structure with scheduled breaks. Content is configurable to your forecasting portfolio, data infrastructure, and renewable asset mix.

#	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	Variational quantum circuits (VQCs) for time-series regression: encoding meteorological features into parameterised quantum circuits Quantum kernel methods (QKE) for pattern recognition in mesoscale weather data Quantum reservoir computing for short-horizon wind and solar output prediction
Break, after 50 min
3	Grid Balancing and Storage Dispatch Optimisation Quantum approaches to real-time energy system management	QAOA for unit commitment and economic dispatch under renewable intermittency Quantum annealing for battery storage charge/discharge scheduling with grid constraints Hybrid quantum-classical pipelines for day-ahead and intraday market bidding
4	Interactive Demonstration: Quantum Forecasting Pipeline Full-day format only	Facilitator-led walkthrough: encoding historical wind farm output data into a VQC forecasting model Interpreting prediction accuracy versus classical LSTM and ARIMA baselines Delegates discuss: identifying which forecasting problems in their portfolio have quantum-compatible structure
Break, after 60 min
5	Current Hardware Limits and Honest Performance Assessment What works now, what does not, and the 2026-2030 frontier	NISQ performance ceiling: feature map sizes achievable today (10-40 qubits with noise mitigation) and impact on forecasting accuracy Published benchmark-specific performance comparisons: QML versus classical ML on energy datasets (IBM, Pasqal, academic groups) Fault-tolerant timeline and what it unlocks for utility-scale forecasting problems
6	Data Infrastructure and Vendor Landscape What you need before engaging a quantum provider	Data pipeline requirements: weather station density, SCADA historian formats, and feature engineering for quantum circuits Vendor comparison: IBM Qiskit, Pasqal, Xanadu PennyLane, D-Wave for forecasting workloads Quantum-inspired classical alternatives: tensor network methods and simulated annealing for near-term deployment
7	Q&A and Pilot Planning

Designed and Delivered By

Workshops are designed and delivered by QSECDEF in collaboration with sector specialists. All facilitators have direct experience in both quantum technologies and power & energy systems.

QSECDEF brings world-leading expertise in post-quantum cryptography, quantum computing strategy, and defence-grade security assessment. Our advisory membership spans 600+ organisations and 1,200+ professionals working at the intersection of quantum technologies and critical infrastructure security.

Power & Energy workshops are co-delivered with sector specialists who bring direct operational experience in power & energy organisations. This ensures workshop content is grounded in regulatory, operational, and technical realities specific to the sector.

Commission This Workshop

Sessions are configured around your forecasting portfolio, renewable asset mix, data infrastructure, and grid balancing requirements. Get in touch to discuss requirements and schedule a date.

Quantum technologies are evolving quickly and new developments emerge regularly. This page was last updated on 15/03/2026. For the most current information about course content and suitability for your organisation, we recommend contacting us directly.

Power & Energy Workshops All Consulting Services All Workshops