Jul 2026· International Journal of Green Energy· Vol 23, pp. 2874 - 2890· 0 citations· 43 references
TL;DR
A hierarchical hybrid spatiotemporal architecture is introduced, which synergistically utilizes dilated causal convolutions to extract local trends and instantaneous fluctuations, while incorporating a multi-head self-attention mechanism to aggregate global context information, thereby achieving a complementary fusion of local and global features.
Abstract
ABSTRACT With the continuous increase in wind power penetration, accurate and reliable short-term wind power forecasting is pivotal for optimizing grid dispatch and ensuring system stability. However, the complex spatiotemporal coupling within wind farms poses severe challenges for forecasting. Most existing Spatiotemporal Graph Neural Networks (STGNNs) are confined to predefined static graph structures, failing to effectively capture the dynamic wake effects induced by wind direction fluctuations and the non-stationarity of spatial distributions. Furthermore, the temporal modules of existing models often struggle to balance the acute capture of local high-frequency abrupt changes with the effective modeling of global long-range dependencies. To address these limitations, this paper proposes a Dynamic Spatiotemporal Convolutional Attention Network (DST-CAN). First, we design a Metric-Learning-based Dynamic Topology Module that reconstructs the time-evolving spatial dependency topology among wind turbines by adaptively fusing physical distance priors with real-time feature similarities. Second, a hierarchical hybrid spatiotemporal architecture is introduced, which synergistically utilizes dilated causal convolutions to extract local trends and instantaneous fluctuations, while incorporating a multi-head self-attention mechanism to aggregate global context information, thereby achieving a complementary fusion of local and global features. Extensive experiments on the real-world datasets demonstrate that DST-CAN significantly outperforms existing methods in multi-step forecasting tasks. Ablation studies further validate the effectiveness of dynamic topology modeling in handling non-stationary wake effects.
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.
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.
S. Marisargunam, T. Mariprasath, Mohit Bajaj et al.· Energy Exploration & Exp...· 0 citations
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.
Yanxia Wang, Weilong Yu, Minghan Ma et al.· Sustainability· 0 citations
Wind power forecasting plays a crucial role in power dispatch and safety management of wind farms. However, the insufficient integration of multimodal heterogeneous data and the limitations of conventional graph construction strategies significantly restrict forecasting performance. Existing approaches either rely on simple feature aggregation, which cannot fully capture cross-modal dependencies, or adopt predefined or single-criterion graph construction methods that fail to characterize complex turbine relationships involving spatial, temporal, and nonlinear correlations. To address these challenges, this paper proposes a Multimodal Adaptive Fusion Graph Neural Network (MAF-GNN) for short-term wind power forecasting. First, a Modality-Aware Representation Learning (MARL) module is developed to extract informative multimodal representations by modeling modality-specific characteristics and cross-modal dependencies through attention-based fusion. Second, an Adaptive Graph Learning with Multi-Similarity (AGL-MS) module is introduced to parametrically integrate four complementary similarity priors—geographic distance, Dynamic Time Warping (DTW), Maximal Information Coefficient (MIC), and cosine similarity—for adaptive turbine correlation graph construction. Furthermore, a Credibility-Modulated Graph Convolutional Network (CM-GCN) is developed to reduce the influence of unreliable node information during message propagation. Extensive experiments conducted on the SDWPF dataset demonstrate that MAF-GNN reduces MAE by 14.0–21.3% compared with sequential baselines and achieves 5.3–10.2% improvement over spatiotemporal graph-based models. Ablation studies further verify the complementary effectiveness of each proposed module in improving forecasting performance.
Guochen Zhang, Qing Ye, Xiaobo Li et al.· Information· 1 citation
Accurate prediction of distributed photovoltaic (PV) output is essential for modern smart grids and electromagnetic energy infrastructure, where renewable generation exhibits strong nonlinearity and long-term temporal dependence due to cloud occlusion and meteorological variations. Traditional forecasting methods often struggle to characterize cross-node interactions and accumulate prediction errors under complex operating conditions. To address these issues, this paper proposes a hybrid spatiotemporal prediction framework integrating an improved Long Short-Term Memory (LSTM) network with dynamic graph embedding for deep feature mining and coordinated forecasting. A multi-layer residual LSTM with adaptive attention first models long-term dependencies and transient fluctuations from preprocessed time series data. A dynamic graph is then constructed according to feeder connectivity and geographical proximity, where Dynamic GraphSAGE generates node embeddings to capture evolving spatial relationships. Temporal features and graph representations are fused through a Graph Attention Network (GAT) and multi-layer LSTM to jointly model spatiotemporal interactions, while TimeGAN-based sparse data completion and Bayesian optimization further enhance robustness and parameter adaptation. Experimental results demonstrate RMSE values of 0.10, 0.12, and 0.13 for 30 min, 3 h, and 6 h forecasting horizons, respectively, with training speed 20% faster than GCN-LSTM, only a 20% RMSE increase under σ = 0.1 noise, and RMSE remaining below 0.14 at unseen sites. The proposed framework provides an effective solution for distributed PV forecasting and offers methodological support for intelligent electromagnetic energy management and resilient smart power systems.
S. Wan, J. Tan, T. Luo et al.· Advanced Electromagnetics· 0 citations
Accurate interpretation of wind turbine operational measurements is essential for reliable wind power forecasting and efficient wind farm operation. Modern wind turbines are equipped with supervisory control and data acquisition (SCADA) systems that continuously record key operational parameters, providing rich measurement data for data-driven modeling. However, although deep learning methods have demonstrated strong capability in capturing nonlinear relationships, most existing approaches lack physical interpretability, show limited robustness under extreme conditions, and often neglect fundamental aerodynamic constraints. To address these limitations, this paper proposes a physics-constrained graph attention network (PCGAT) for multi-horizon mid-to-long term (from 6 h to 3 d) wind turbine power forecasting using SCADA measurement data. The proposed model constructs a temporal graph in which each node represents a measurement time step and connects to its preceding neighbors to capture dynamic dependencies, while a multi-head graph attention mechanism extracts informative representations from the graph-structured time series. A hybrid loss function incorporating aerodynamic constraints is introduced to ensure physical consistency during model training. Experiments on real-world SCADA datasets demonstrate that the proposed approach achieves higher forecasting accuracy and stronger robustness than several state-of-the-art models across multiple prediction horizons.
Menglong Wu, Xiaotian Zhang, Wenfei Liu et al.· Measurement science and tech...· 0 citations