2026· Energy Engineering· pp. 1-10· 0 citations· 38 references
TL;DR
A novel hybrid intelligent framework—integrating Improved Lotus Effect Algorithm, Variational Mode Decomposition, and ensemble deep learning—specifically designed for ultra-short-term wind power prediction in energy dispatch applications is engineers, which employs elite chaotic opposition-based learning to autonomously optimize VMD decomposition levels.
Abstract
: The accelerating penetration of variable renewable generation into electrical grids necessitates advanced forecasting paradigms capable of addressing stochastic intermittency challenges. This paper engineers a novel hybrid intelligent framework—integrating Improved Lotus Effect Algorithm (ILEA), Variational Mode Decomposition (VMD), and ensemble deep learning—specifically designed for ultra-short-term wind power prediction in energy dispatch applications. Unlike conventional approaches relying on static parameterization, the proposed methodology employs elite chaotic opposition-based learning to autonomously optimize VMD decomposition levels, thereby decoupling non-stationary wind power sequences into stationary sub-components without empirical intervention. Local temporal feature extraction is subsequently performed via one-dimensional Convolutional Neural Networks (1D-CNN), wherein sliding convolutional kernels operate along the temporal axis to capture localized patterns—such as ramp events, gradient transitions, and short-term fluctuations—embedded within the univariate wind power sequence. Temporal dependencies are subsequently modeled through Bidirectional Long Short-Term Memory (BiLSTM) networks enhanced with attention mechanisms. An Adaptive Boosting (AdaBoost) ensemble strategy further aggregates multiple weak regressors to fortify prediction robustness against ramp events and turbulent meteorological conditions. Comprehensive validation utilizing Belgian grid operational data demonstrates that the proposed architecture achieves substantial error reductions, attaining Root Mean Square Error (RMSE) of 2.2496 MW, Mean Absolute Error (MAE) of 1.9897 MW, and Symmetric Mean Absolute Percentage Error (SMAPE) of 5.26%, with a Coefficient of Determination (R) 2 coefficient approaching unity (0.9962). These results underscore the framework’s practical efficacy for grid stabilization, load frequency control, and energy management decision support systems operating under high renewable penetration scenarios.
A deep learning framework based on a Mamba-driven state-space model architecture for comprehensive PQ disturbance classification is proposed and results indicate that the proposed method is well-suited for real-time smart grid monitoring and intelligent protection systems.
Pintu Das, Chandan Jana, Sannistha Banarjee et al.· Engineering Research Express· 0 citations
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
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.
Yu-Hang Zhang, Yi-Ting Zhao, Yu-Jing Meng et al.· De Computis· 0 citations
With the large‐scale integration of wind power, its inherent intermittency and uncertainty pose significant challenges to power system operation. Existing probabilistic forecasting methods often struggle with capturing spatiotemporal correlations among multiple turbines as well as generalizing to unseen turbines or future time steps. To address these issues, we propose a novel multi‐turbine collaborative probabilistic forecasting framework based on the flow matching enhanced neural process. By formulating wind power forecasting as a conditional stochastic process, our method naturally enables cross‐turbine knowledge sharing through a Transformer‐based encoder. The introduction of flow matching allows efficient single‐ or few‐step sampling while avoiding the usage of variational lower bounds, and hence is more effective. Experiments on a real‐world dataset comprising 134 turbines over 245 days demonstrate that the proposed framework achieves superior performance, significantly outperforming existing neural process variants and deep learning baselines in terms of RMSE, MAE, and continuous ranked probability score.
Jiabei Liu, Zheren Zhu, Le Yao et al.· International Journal of Ada...· 0 citations
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.
Peng Chen, Danhong Zhang, Yixin Su· International Journal of Gre...· 0 citations
Accurate electricity load forecasting is essential for smart-grid scheduling, yet existing deep learning methods often struggle to capture multiple temporal scales efficiently. This paper proposes an adaptive multi-scale decomposition (AMD) framework comprising three modules. Multi-scale decomposable mixing extracts hierarchical temporal representations through downsampling and residual mixing. Dual dependency interaction captures temporal dependencies and variable coupling through parallel time and channel mixing. Adaptive multi-predictor synthesis combines scale-specific predictions using autocorrelation-based soft Top-K routing, emphasizing dominant scales while retaining contributions from other scales. Experiments on six electricity-domain benchmark datasets, including Electricity Consuming Load, Electricity Transformer Temperature (ETT), and Solar Energy, show that AMD achieves an average mean squared error (MSE) of 0.28 and mean absolute error of 0.33 for long-term forecasting, providing performance comparable to TimeMixer. AMD performs favorably on Solar Energy and long-horizon Electricity Consuming Load (ECL) tasks but is marginally outperformed by TimeMixer on ETTh2, where trend-dominated patterns favor fixed multi-scale weighting. With 2.3 million parameters, AMD requires 45.2 s per training epoch and 12.5 ms per inference, placing it within the same efficiency range as TimeMixer and below Transformer-based baselines. Ablation, sensitivity, and visualization analyses confirm the contributions of all three modules and reveal that daily and hourly scales account for 35.8% and 28.5% of scale attribution, respectively. Overall, AMD offers a competitive accuracy-efficiency trade-off for electricity-load benchmark forecasting under mixed or shifting temporal periodicities. Its applicability to real-world smart-grid operations requires further validation.
Gao-Jian Wang· Energy Exploration & Exp...· 0 citations
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