The results show that using lagged variables allows the model to learn the temporal dependencies of wind speed more effectively and produce competitive RMSE-based prediction performance, while the persistence model remains competitive according to MAE and R.
The results validate that ensemble learning models are more reliable for sparse Meteorological datasets, whereas DL models are effective for capturing short-term temporal patterns, and provides a practical forecasting framework for wind farms, offering improved operational planning, reduced forecasting uncertainty.
Given the problems of non-stationary power time series, response lag, and amplified prediction errors due to sudden changes in wind direction under transitional meteorological conditions, this study proposes a wind power forecasting model that integrates multiscale time-series features, transition-aware attention mecha...
A U-shaped spatiotemporal feature fusion network named U-STNet is developed, which realizes joint modeling of inter-turbine spatial correlations and multi-period long-range temporal dependencies for wind speed forecasting and verifies the effectiveness of jointly modeling turbine spatial topology and multi-scale tempor...
The Bayesian optimization-based CNN-GRU combined model (BO-CNN-GRU) is used to predict the long-term wind power in a low-wind-speed wind farm and has higher prediction accuracy and the new model has a good application effect.
Accurate wind forecasting is critical to ensure stable and efficient integration of renewable energy resources in modern power systems. However, the inherent variability and non-stationarity of wind pose a significant forecasting problem for modern power system operators to ensure power system stability. A new hybrid f...
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