Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction

A physics-constrained temporal modeling framework for robust short-term wind power forecasting

Accurate short-term wind power prediction plays a critical role in ensuring stable grid operation, effective energy management, and the large-scale integration of renewable energy systems under highly variable wind conditions. Although data-driven and deep learning models have demonstrated promising forecasting capability, many existing approaches suffer from performance degradation during rapid wind fluctuations due to the lack of embedded physical constraints and the high computational complexity associated with recurrent architectures. To address these limitations, this article proposes a Physics-Guided Residual Temporal Convolutional Network (PG-ResTCN) for short-term wind power forecasting. The proposed framework integrates dilated temporal convolutional learning with residual connections to effectively capture multiscale temporal dependencies in wind power time series while avoiding the sequential computation of recurrent neural networks, thereby improving computational efficiency. Furthermore, a physics-based smoothness constraint is incorporated into the training loss function to enforce physically consistent power ramping behavior that reflects the inherent operational dynamics and inertia of wind turbines, reducing unrealistic fluctuations in predicted power outputs. The effectiveness of the proposed model is validated using a large real-world dataset containing approximately 140,161 hourly samples collected from five wind farm locations, including meteorological variables and corresponding turbine power outputs. Comprehensive experiments are conducted by comparing the proposed method with widely used benchmark models, including Support Vector Regression, Random Forest, Extreme Gradient Boosting, and Long Short-Term Memory networks. Results demonstrate that the proposed PG-ResTCN model achieves superior forecasting performance, obtaining a root mean square error of 0.0359, mean absolute error of 0.0296, and R 2 of 0.9759, outperforming all baseline models. The integration of physics-guided constraints with the temporal convolutional architecture significantly enhances prediction accuracy, stability, and generalization capability. In addition, the proposed framework maintains high computational efficiency and robustness, making it suitable for real-time wind energy forecasting applications, intelligent energy management systems, and microgrid power system operations.

Optimization and Accuracy Improvement of Power Forecasting Models for Wind Farms Under Transient Weather Conditions

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 mechanisms, physical constraints on turbine operation, and dynamic residual correction. To improve the model's ability to jointly characterize different scales of meteorological evolution and power lag characteristics, a feature extraction network with short-, medium-, and long-term branches based on TCN (Temporal Convolutional Network) is built, which incorporates bidirectional GRU and Transformer architectures; additionally, the feature weights at each scale are dynamically adjusted according to the severity of inflection points, and the prediction output is constrained by air density, power curves, operational status and ramping limits. Residual caching and time-delay gating are employed to correct for lag errors in the range of 1 to 6 sampling steps. 50,842 valid data sets from a wind farm were used for validation. The general MAE and RMSE of the model were 0.258 MW and 0.386 MW, respectively, and these were lower than those of the baseline TCN by 21.58% and 20.90%. In the transition period, the MAE and RMSE were 0.305 MW and 0.448 MW; these had been reduced by 22.19% and 23.29%. Therefore, the developed model can reduce peak deviations caused by abrupt changes in weather and improve the accuracy and stability of wind power forecasting under all operating conditions.

Initial-State-Aware Multi-Scale Transformer for Short-Term Wind Power Forecasting

Accurate short-term wind power forecasting is essential for the secure and economic operation of power systems with high renewable energy penetration. However, forecasting performance is still affected by meteorological forecast uncertainty and the complex multi-scale fluctuation characteristics of wind power generation. To address these challenges, this paper proposes an Initial-State-Aware Multi-Scale Transformer framework for 12 h-ahead wind power forecasting. The principal methodological contribution is an initial-state-aware meteorological representation and progressive fusion strategy tailored to weather-driven wind power forecasting. The framework explicitly distinguishes the atmospheric state available at forecast initialization from the subsequent forecast meteorological trajectory and uses the former to condition the representation of the latter through cross-attention and gated residual fusion. The resulting meteorological representation is then progressively coupled with coarse- and fine-scale historical power representations, and a horizon-oriented forecasting head generates the future power sequence in parallel. Experiments on three wind farms demonstrate that the proposed method achieves the best overall forecasting performance. Compared with the strongest baseline model, it reduces NMAE and NRMSE by 8.54% and 2.36%, respectively.

Fast High-Resolution Wind-Field Prediction over Complex Terrain Using WRF-CFD Simulations and a Time-Aware Neural Network

High-resolution wind-field prediction over complex terrain is important for wind-energy assessment, grid safety, and hazard mitigation. This study uses a coupled Weather Research and Forecasting–Computational Fluid Dynamics (WRF–CFD) workflow for the Askervein Hill benchmark. Driven by the fifth-generation European Centre for Medium-Range Weather Forecasts Reanalysis dataset, WRF provides mesoscale atmospheric conditions, and CFD resolves the terrain-induced flow features at high spatial resolution. Because repeated WRF and CFD simulations are computationally expensive, we develop a time-aware neural-network surrogate model, termed the multilayer perceptron-prior gated temporal correction (MLP-GTC) model, to predict the resulting three-component wind field. The model first learns the local wind response associated with terrain and inlet conditions, and it then applies a gated temporal correction based on recent inlet conditions and their corresponding pointwise predictions. The data are divided chronologically into 683 training, 146 validation, and 147 test time steps, with 5187 fixed spatial points evaluated at each time step. The coupled WRF–CFD simulations reproduce the observed wind structure with a maximum Pearson correlation coefficient of 0.96 and a 10 m wind-speed root mean square error of 0.84 m/s. For the three-component velocity field, MLP-GTC achieves validation/test root mean square errors of 0.282/0.310 m/s and mean absolute errors of 0.179/0.199 m/s. It improves on the multilayer perceptron model (0.486/0.516 m/s) and the matched five-step single-layer long short-term memory network (0.334/0.438 m/s).

Spatiotemporal Feature Fusion Using U-Shaped Architecture for Accurate Wind Speed Prediction

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 temporal dependencies for wind speed forecasting.

Hybrid transformer model for wind power generation forecasting