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.
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
With the rapid growth of wind power penetration, the inherent randomness and uncertainty of wind power pose serious challenges to the stable operation of power systems. To address this issue, this paper proposes a wind power forecasting model based on dual decomposition. The model first applies Variational Mode Decomposition (VMD) to the original multivariate input sequences to decompose them into multiple modal components with different frequency characteristics. Subsequently, the Elastic Net (EN) algorithm is employed to denoise the decomposed features and select key variables, thereby reducing data non-stationarity and improving feature quality. Next, Adaptive Multi-Seasonal Trend Decomposition (ADMSTL) is applied to the target series to further extract trend and seasonal information, enhancing the model’s capability to represent complex temporal features. Finally, the features obtained from the dual decomposition are fused and used as input to an Informer-based forecasting model. Experimental results based on real operational data from a wind farm in Xinjiang, China, show that the proposed method achieves high stability and prediction accuracy in multistep forecasting tasks, validating the model’s effectiveness and superiority.
Unknown authors· European Conference on Elect...· 0 citations
Accurate wind power forecasting is essential for renewable-energy accommodation, low-carbon dispatch, and the sustainable operation of modern power systems. However, wind power series exhibit pronounced non-stationarity, strong volatility, and multi-scale evolution, making long-term trends and short-term disturbances difficult to characterize jointly. In addition, multi-step forecasting errors tend to accumulate with increasing horizons, degrading model accuracy and stability. To address these issues, this study proposes CoFFormer, a collaborative frequency-domain-enhanced network for non-stationary wind power forecasting. The model reduces input modeling complexity, strengthens collaborative representation of heterogeneous temporal information, and suppresses output-stage error accumulation. Specifically, embedded series decomposition mitigates coupling interference between trend and fluctuation components. Differentiated temporal modeling and dynamic gating then adaptively coordinate the contributions of different feature representations, while frequency-domain residual compensation enhances the recovery of periodic structures and local oscillations. Experiments on ETTh2, wind_speed, WindPower, and Location2 demonstrate strong competitiveness across forecasting horizons. CoFFormer achieves MSE/MAE values of 0.0957/0.2238 and 0.1508/0.2889 on ETTh2 for 12- and 24-step forecasting, and 0.0617/0.1490 and 0.3838/0.3948 on WindPower for 3- and 24-step forecasting, outperforming most baselines. Ablation studies confirm the effectiveness and synergy of each component, providing an effective solution for high-accuracy multi-step forecasting of complex non-stationary wind power series.
The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.
M. S. Song, C. Yang, Z. Heng et al.· Advanced Electromagnetics· 0 citations
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.
Photovoltaic (PV) power generation is affected by meteorological and geographical factors, displaying volatility, nonlinearity, and characteristics across multiple timescales. Accurate short-term forecasting of PV power is essential for enhancing PV resource utilization in the power grid and maintaining the quality of renewable energy integration. This paper introduces a Multi-Variable Time-Frequency Synergistic Fusion network model, enhanced by the Triangular Wave Zebra Algorithm (TWZA), for short-term PV power forecasting. To tackle the challenges of disjointed time-frequency feature fusion and limited generalization in complex fluctuation scenarios, a dual-parallel branch structure is developed to synergistically model temporal and spectral features in both time and frequency domains. In the time-domain branch, a kLSTM encoder is employed, which integrates block embedding and a Multi-Variable Correlation Attention mechanism to uniformly model variable coupling relationships and long-term dependencies. The frequency-domain branch incorporates the frequency decomposition architecture of the Multi-order Kolmogorov–Arnold Network (KAN), decomposing the power signal into multi-scale frequency components. By combining multi-order KAN representation learning with deep separable convolutions, it extracts nonlinear spectral features. A cross-domain attention-based time-frequency fusion mechanism is developed for adaptive and collaborative integration of time-domain and frequency-domain features. Second, to address the inability of time-frequency models to adaptively tune hyperparameters, we introduce TWZA to adaptively optimize time-frequency fusion weights, network hyperparameters, and convolution kernel sizes. This enhances the model's global search and local exploration capabilities while reducing training loss. Extensive experimental validation demonstrates that the proposed model outperforms all baseline models in terms of R2, root mean square error, and mean absolute error, achieving improvements of at least 18.2%, 27.3%, and 14.9%, respectively.
Wan-Nian Wei, Zhiwen Wang, Haoxu Wang et al.· Journal of Renewable and Sus...· 0 citations
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