A Dual-Track Feature-Enhanced Physics-Informed Model for Accurate Wind Power Forecasting with Physical Consistency

Multi-site Wind Energy Prediction System Based on Power Decomposition and Deep Model Integration

A multi-site wind power forecasting system based on power decomposition and deep model ensemble that applies Variational Mode Decomposition (VMD) to separate raw power sequences into high-frequency and low-frequency components, each directed into a structurally symmetric dual-branch framework.

An Ultra-Short-Term Wind Farm Power Forecasting Method Incorporating Spatial Features for Sustainable Energy Integration

Accurate wind power forecasting is imperative for ensuring grid stability and facilitating the large-scale integration of renewable energy—both central pillars of the global energy transition and the Dual Carbon strategic goals. However, existing methods often fail to fully capture the spatial heterogeneity and interdependencies among individual turbines, limiting their effectiveness for sustainable grid operation. To address this gap, this paper proposes an ultra-short-term wind power forecasting framework that incorporates explicit multi-dimensional spatial features. At the feature level, a 12-dimensional spatial feature system is constructed to quantify the microscale topology of wind farms. These static spatial attributes are seamlessly fused with dynamic temporal data using a dimensionality-balance factor strategy. Finally, a hybrid deep learning network comprising a multi-scale CNN, a multi-layer BiLSTM, and a multi-head self-attention mechanism is developed to capture complex spatiotemporal patterns. Experimental results on three real-world datasets show that the proposed method significantly outperforms baseline models, reducing the Mean Absolute Percentage Error by up to 11.09% and improving the coefficient of determination R2 up to 0.9120. By improving forecast accuracy and robustness, the method directly supports more reliable grid dispatching, reduces curtailment of wind energy, and thus contributes to the sustainable utilization of renewable resources. These findings demonstrate that incorporating explicit spatial correlation effectively enhances the accuracy and robustness of ultra-short-term wind power forecasting, providing robust decision support for power grid dispatching and advancing the sustainability of modern power systems.

Physics-Informed Dual-Attention BiGRU for Hourly Wind Speed Prediction

The proper estimation of the near-term wind speed is one of the primary requirements to convert the power produced by wind into the electricity networks reliably. The current hybrid architectures which split the wind signal into sub-components before prediction, in spite of their competitive error measures, are costly to process and run the risk of contamination of the temporal information at the decomposition frontiers. The proposed paper suggests Physics-Informed Dual-Attention Bidirectional Gated Recurrent Unit (DA-BiGRU), which is a single-stage model designed to predict the one-hour-ahead wind speed, without explicit signal decomposition at all. Physical knowledge is added using three analytically calculated variables of the atmosphere the vertical wind shear exponent, near-surface air density, and hub-height turbulence intensity that enhance the unstructured sensor channels as structured domain-sensitive inputs. The cascaded dual-attention design is selective in the information it weights and therefore the weighted information is determined by two gates; a feature level gate that increases or decreases the relative importance of each given input variable and a time level gate which emphasizes the most predictive relevant historical instances of a 24 step lookback window. Operational record experiments of a wind farm give Mean Absolute Percentage Error (MAPE) of 5.50% and a coefficient of determination $\left(R^{2}\right)$ of 0.9873, both performing better than a naive persistence model and a rolling-decomposition LSTM benchmark. These results affirm the fact that implementing atmospheric physics into the input layer provides a computationally manageable and precise forecasting resolution that is appropriate in real-time grid application.

Enhancing Wind Power Forecasting via Multi-Farm Coupling Under Data Isolation: A Physics-Guided Personalized Federated Approach

Accurate wind power forecasting is critical for grid stability and long-term sustainability, but mountainous wind farms face challenges from complex micro-meteorology, restricted communication, and non-IID data, exacerbated by data silos that prevent centralized learning. Most federated learning relies on data-driven averaging that ignores multi-farm coupling, or adopt complex local models that increase communication overhead. To address these, a physics-guided personalized federated approach is proposed to enhance wind power forecasting. Its core is a physics-guided aggregation mechanism that constructs a dynamic weight matrix from distance, elevation, and real-time wind direction to enable personalized aggregation capturing multi-farm coupling. The federated framework combines a shared CNN-LSTM with multi-head attention for regional patterns and a personalized layer for local microclimate. A risk-aware asymmetric loss is incorporated to penalize high-power errors, enhancing operational reliability under high-power conditions. Validation on mountainous wind farms for 3-day forecasting under typical and extreme scenarios across wet, dry, and normal seasons shows that the average R2 exceeds 0.95, and the average RMSE is reduced by more than 24% compared to baselines, achieving high accuracy under strict privacy preservation. By enabling multi-farm coupling under data isolation, this approach achieves high forecasting accuracy on the studied wind farms, showing promise for similar ones.

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.

Hybrid transformer model for wind power generation forecasting