Aug 2026· Distributed Generation & Alternative Energy Journal· 0 citations
TL;DR
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.
Abstract
To address the limitations of existing wind power forecasting methods in mixed-frequency signal modeling, multi-site spatial correlation capture, and single-model generalization, this paper proposes a multi-site wind power forecasting system based on power decomposition and deep model ensemble. The system 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. Within each branch, three heterogeneous sub-models – ConvGAT-LSTM, Spectral Transformer, and TCN – operate in parallel; branch outputs are aggregated by simple averaging and linearly combined into a base prediction, which is subsequently refined by an XGBoost residual correction layer. Experiments on six Chinese wind farm datasets demonstrate that the proposed system achieves an average RMSE of 0.1065±0.0021 and R2 of 0.8335±0.0167 at the 24-step forecast horizon, improving over the state-of-the-art baseline TCOAT by 1.4% and 0.5%, respectively. The VMD module alone contributes an 11.6% RMSE reduction, and the system reduces local RMSE during ramp events at Site 6 by 34.9% relative to the baseline. Ablation experiments and statistical significance tests (p<0.05, Cohen’s d>0.82) confirm that each module contributes meaningfully to overall performance.
An ultra-short-term wind power forecasting model based on a two-stage signal decomposition and a hybrid architecture combining iTransformer, LSTM, and KAN that achieves clear improvements in forecasting accuracy and fitting capability is proposed.
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.
Accurate wind power forecasting is essential for advancing sustainable wind energy utilization, stabilizing new energy power systems, and facilitating the low-carbon and sustainable development of the power industry. Aiming to address the challenges of strong non-stationarity, significant multiscale fluctuations, and the difficulty in jointly modeling local dynamics and long-term dependencies in wind power time series, this study proposes a short-to-medium-term wind power forecasting method based on multiscale decomposition and local-global cooperative modeling. First, Seasonal Trend Decomposition (STL) is employed to extract the trend and seasonal components from the raw time series. The residual series is then subjected to Adaptive Noise-Complemented Empirical Mode Decomposition (CEEMDAN) to reveal the underlying non-stationary characteristics. Based on the dominant frequency characteristics, the decomposed components are reconstructed into high, medium, and low-frequency series, which, together with the trend and seasonal components, form a multidimensional set of input features. Second, to accommodate the time-series characteristics of the multiscale components, we designed an LCNN-Mamba forecasting framework that integrates a Local Convolutional Neural Network (LocalCNN) with a state-space model. The LocalCNN is used to model local and multiscale dynamic features, while the multi-layer Mamba state-space module efficiently captures long-term time-series dependencies, thereby achieving a synergistic enhancement of both local sensitivity and global modeling capabilities. The proposed model was validated using actual operational data from wind farms at two different locations. Experimental results show that the proposed approach outperforms traditional forecasting methods in terms of forecasting accuracy, providing a reliable technical reference for wind power grid-connected operation, power dispatching optimization, and the large-scale and high-quality sustainable development of wind energy resources.
Fan Yang, Jiaying Zhang· Sustainability· 0 citations
Experiments on ERA5 data show that AMBHFN outperforms eight retrained baselines over the 0–23 h forecast horizon, with an average error reduction of more than 12%.
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
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