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.
Accurate wind power forecasting is an essential prerequisite for ensuring the safe and stable operation of power systems and improving the scheduling and planning capability of power grids. To address the large prediction errors caused by the strong nonlinearity and non-stationary fluctuations of wind power time series, as well as the coupling effects among multiple meteorological variables, this paper proposes a wind power forecasting model integrating multi-scale decomposition, dual dependency interaction, and cross-variable linear mapping. The multi-scale decomposition module employs multi-scale average pooling to separate the trend and periodic components of the sequence, thereby effectively mitigating the non-stationary interference of the original series. The dual dependency interaction mechanism explores long-term temporal correlations and coupling relationships among meteorological factors from both temporal and variable dimensions. Finally, a cross-variable linear structure is adopted to accomplish prediction. Experiments are conducted using annual measured data collected from a wind farm in Inner Mongolia, China. The proposed model is compared with several mainstream forecasting models, including Informer, xLSTM-Informer, GRU, and CNN-LSTM. Experimental results demonstrate that the proposed model achieves MSE, RMSE, MAE, and R2 values of 10.32, 3.214, 1.966, and 0.961, respectively. Compared with the xLSTM-Informer model with the best overall baseline performance, the proposed model reduces MAE by 7.35% and cuts training time by 97.51%, thus conclusively demonstrating that the proposed method achieves much better forecasting accuracy without sacrificing training efficiency.
Yuefeng Lu· 2026 5th International Confe...· 0 citations
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
Electricity load forecasting is essential for balancing energy supply and demand, reducing energy waste, and maintaining power grid stability. Accurate forecasts enable power utilities to optimize energy dispatch and mitigate the risk of supply shortages or outages. However, conventional forecasting methods often struggle to capture highly nonlinear local fluctuations in electricity consumption and long-term temporal dependencies. To address these challenges, this study proposes MSCNN-ResLSTM, a hybrid model for multi-step electricity load forecasting. The proposed model integrates Multi-Scale Convolutional Neural Networks (MSCNNs) to extract local time-series features at multiple temporal scales, residual blocks (ResBlocks) to enhance feature representation through residual connections, and Long Short-Term Memory (LSTM) networks to model long-range temporal dependencies. To comprehensively evaluate its effectiveness, a cross-paradigm experimental framework is established in which MSCNN-ResLSTM is compared with seven representative benchmark models from three methodological categories: traditional machine learning (Extreme Gradient Boosting-XGBoost), classical recurrent and convolutional neural networks (LSTM, Temporal Convolutional Network-TCN, CNN-LSTM, MSCNN-LSTM, and Direct LSTM (Seq2Seq)), and self-attention-based architectures (Transformer). Experimental results show that MSCNN-ResLSTM achieves higher forecasting accuracy and greater stability across the full 24-step prediction horizon, consistently outperforming all competing baselines while effectively suppressing recursive error propagation.
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 hybrid FCM-WGM-BiLSTM-Transformer (FW-BTP) framework integrating Fuzzy C-Means clustering, Weighted Grey Model (WGM) trend extraction, and a coupled BiLSTM-Transformer module is proposed, supporting refined scheduling in modern power systems.
