A Stable Rolling Forecast for Renewable Energy Generation Based on a Neural Ordinary Differential Equation

Accurate and long-horizon forecasting is essential for reliable planning of solar and wind power generation. Most existing models rely on high-resolution meteorological data, which are often unavailable in practical microgrid environments. This study proposes SNORF, a solar–wind neural ordinary differential equation model that uses only basic meteorological variables such as solar radiation, cloud cover, and wind speed together with lagged generation values. However, reliance on lagged generation inherently limits the effective forecasting horizon of conventional models. To address this limitation, SNORF extends the forecasting horizon while maintaining accuracy and stability through a rolling forecasting framework with a bounded input-dependent drift function and repeated Euler integration that promotes smooth hidden-state dynamics. SNORF supports both deterministic point forecasting and probabilistic forecasting through quantile-based loss functions. Experiments on solar and wind power datasets show that SNORF consistently outperforms representative time-series forecasting models. For solar forecasting, SNORF reduces RMSE and MAE by 15.35% and 16.93% on average compared with baseline models. For wind forecasting, the improvements reach 44.77% in RMSE and 52.85% in MAE on average. Furthermore, when evaluated under the official protocol of the 2024 IEEE Hybrid Energy Forecasting and Trading Competition, SNORF achieves a top 4% ranking using only the officially provided basic weather dataset, demonstrating its practical applicability to renewable energy forecasting in microgrids and virtual power plants.