Hybrid Neural Operator and Physics-Informed Learning for Renewable Energy Forecasting

Forecasting of local renewable energy variables such as solar irradiance and wind speed is critically important for operational grid management and energy markets. We present a hybrid machine learning model that combines Adaptive Fourier Neural Operator (AFNO) architectures with physics-informed loss constraints, designed to capture both learned spatial–temporal patterns and key physical relationships in atmospheric fields. The model is trained on reanalysis and high-resolution observational datasets over the Baltic region and evaluated in comparison with baseline statistical and numerical weather prediction benchmarks.

Our contributions include: (i) a hybrid modelling strategy that enforces approximate physical consistency via penalised residuals of key balance equations during training; (ii) a detailed benchmarking framework for lead-time dependent forecast skill on solar and wind energy generation targets; and (iii) an assessment of uncertainty and calibration properties using probabilistic scoring metrics. Results are evaluated against numerical weather prediction baselines, highlighting the strengths and limitations of the hybrid approach and outlining a viable pathway for future improvements in sub-daily renewable energy forecasting.

This work contributes to the session’s themes of advanced machine learning and statistical forecasting methods in geosciences and highlights the potential of hybrid approaches for enhancing short-term predictive skill.