A Hybrid Deep Learning Framework with Adaptive Signal Decomposition for Enhanced Ultra-Short-Term Wind Power Forecasting in Power System Operations
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.