A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting

Modular approach outperforms monolithic models: The paper demonstrates that separating forecasting and explanation into distinct stages is more effective than combining them into single foundation models, challenging the trend toward all-in-one AI systems for energy prediction.

Quantum-inspired methods show practical promise: A quantum-inspired variational kernel achieved performance within one percentage point of classical baselines across three different geographic regions (Iberian solar, North Sea wind, and Texas mixed generation), suggesting quantum approaches may offer practical value without requiring full quantum computers.

Cross-region generalization remains challenging: The framework was tested across three climatically different regions, indicating the authors recognize that most published forecasters are "tuned and evaluated on a single climatic regime"—a key limitation in responsible AI deployment for energy systems.

AI explainability as a separate layer: Rather than relying on inherent model interpretability, the framework uses generative AI specifically for producing structured natural language explanations of forecasts, treating explainability as a distinct responsibility rather than an afterthought.

Transparent data sourcing supports reproducibility: The framework acquires data through public APIs and uses open public archives, enabling external verification and reducing barriers to responsible AI validation in the energy sector.