Accurate one-hour-ahead wind speed prediction is critical for the efficient use of wind energy, energy generation planning, and the reliable integration of wind energy systems. However, the time-dependent variability and nonlinear behavior of wind speed make this prediction problem challenging. In addition, abrupt wind...
Wind energy represents one of the important sources of renewable energy (RE) and plays a pivotal role in the international decarbonization of energy systems. The novelty of the research work lies in the development and evaluation of a multi-horizon forecasting strategy under sparse-data conditions, where limited but...
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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