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Advances in Wind Engineering 2025年

作者： Hui Liu Rui Yang Institute of Artificial Intelligence and Robotics (IAIR) Key Laboratory of Traffic Safety on Track of Ministry of Education School of Traffic and Transportation Engineering Central South University Changsha 410075 Hunan China

Accurate and reliable short-term wind speed forecasting is essential for the effective utilization and scientific dispatch of wind energy. In recent years, with the rapid development of Artificial Intelligence (AI) technology, the capabilities of prediction models have been significantly improved. However, there are still many challenges in dealing with non-stationary signals, coordinating multi-objective conflicts, and enhancing model robustness. This paper reviews the three core links in wind speed forecasting: multi-scale signal decomposition, hybrid modeling framework, and multi-objective optimization algorithm. First, this paper systematically summarizes common signal decomposition methods, including Empirical Mode Decomposition (EMD), Variational Mode Decomposition (VMD), and wavelet transform-based techniques, and analyzes the characteristics of these methods in terms of frequency resolution, adaptability, and model ensemble capabilities. Secondly, from the perspective of structural form and functional complementarity, this paper explores how to combine shallow models, deep neural networks, and ensemble strategies to build an efficient hybrid prediction model. Finally, a variety of multi-objective Optimization (MOO) algorithms are evaluated, such as the Non-Dominated Sorting Genetic algorithm II (NSGA-II), the multi-objective Particle Swarm Optimization algorithm (MOPSO), and the Strength Pareto evolutionary algorithm II (SPEA-II). Their potential in model architecture adjustment, fusion strategy design, and balancing prediction accuracy, stability, and computational efficiency is analyzed. This paper comprehensively evaluates related studies according to the "decomposition-prediction-optimization" process. At the same time, it emphasizes the importance of developing a wind speed prediction system that is robust, interpretable, and easy to deploy. This study provides a unified perspective and reference framework for building intelligent and goal-oriented wind

关键词： ADMM Alternating Direction Method of multipliers AI Artificial Intelligence ANN Artificial Neural Networks AutoML Automated Machine Learning AVMD Adaptive Variational Mode Decomposition BEMD Bivariate or Complex Empirical Mode Decomposition CEEMD Complementary versions such as Complete Ensemble Empirical Mode Decomposition CEEMDAN Complete Ensemble Empirical Mode Decomposition with Adaptive Noise CFD Computational Fluid Dynamics CNN Convolutional Neural Networks CWT Continuous Wavelet Transform DFT Discrete Fourier Transform DRL Deep Reinforcement Learning ECMWF European Centre for Medium-Range Weather Forecasts EEMD Ensemble Empirical Mode Decomposition ELM Extreme Learning Machines EMD Empirical Mode Decomposition ESN Echo State Network FFT Fast Fourier Transform GDE3 Generalized Differential Evolution 3 GNNs Graph Neural Networks GRU Gated Recurrent Unit LES Large Eddy Simulation IMFs Intrinsic Mode Functions LightGBM Light Gradient Boosting Machine LSTM Long Short-Term Memory MAE Mean Absolute Error MEMD multivariate Empirical Mode Decomposition MODWT Maximal Overlap Discrete Wavelet Transform ε -MOEA epsilon multi-objective evolutionary algorithm MOEA/D multi-objective evolutionary algorithm based on Decomposition MOO multi-objective Optimization MOPSO multiple objective Particle Swarm Optimization MRVMD multi-resolution Variational Mode Decomposition MVMD multivariate Variational Mode Decomposition NSGA-II Non-Dominated Sorting Genetic algorithm II NWP Numerical Weather Prediction PAES Pareto Archived Evolution Strategy PINNs Physics-Informed Neural Networks RANS Reynolds-Averaged Navier–Stokes RL Reinforcement Learning RMSE Root Mean Square Error SPEA-II Strength Pareto evolutionary algorithm II SSA Singular Spectrum Analysis SVD Singular Value Decomposition SVM Support Vector Machines SVR Support Vector Regression VMD Variational Mode Decomposition WD, Wav elet Decomposition WRF Weather Research and Forecasting WT Wavelet Transform XGBoost eXtreme Gradient Boosting Artificial Intelligence Wind speed forec

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