Mohammed Reham H, El-Saieed Asmaa Mohamed
Department of Electrical Computer and Control Engineering, Faculty of Engineering, Suez Canal University, Ismailia, 41522, Egypt.
Department of Communication and Electronics, Mansoura High Institute of Engineering and technology, Mansoura, Egypt.
Sci Rep. 2025 Jul 17;15(1):25962. doi: 10.1038/s41598-025-10641-7.
Accurate wind speed forecasting plays a key role in supporting renewable energy systems, improving flight safety, and enhancing weather prediction. However, the variability and non-stationary nature of wind patterns make reliable forecasting a difficult task. To address these issues, this research proposes a hybrid approach that combines Wavelet Transform (WT) decomposition, an Encoder-Decoder Transformer, and XGBoost in an ensemble setup. To fine-tune model parameters efficiently, the method incorporates the Chaotic Billiards Optimizer (CBO) alongside the Adam optimizer. In this framework, WT helps break down wind speed signals into different frequency bands, capturing both short-term changes and long-term behavior. The Transformer model focuses on learning complex time-based dependencies, while XGBoost adds robustness to the final predictions by reducing overfitting and improving generalization. The model forecasts wind speed values on an hourly basis, up to 24 h ahead. The use of CBO ensures efficient convergence with minimal parameter tuning, making the model suitable for large-scale datasets compared to conventional optimizers, including Adam, Particle Swarm Optimization (PSO) and Genetic Algorithms (GA). When tested on real wind speed data collected from Egypt via the Open-Meteo platform, the proposed model showed strong performance. It achieved a Mean Absolute Error (MAE) of 0.0218, Mean Squared Error (MSE) of 0.0008, and Root Mean Squared Error (RMSE) of 0.0290, along with an R² score of 0.9625, MAPE of 11.97%, and an Explained Variance Score (EVS) of 0.9521. These results were better than those from models like Linear Regression, SVR, LSTM, and even standalone Transformer and XGBoost. Overall, this hybrid method provides a reliable and efficient solution for wind speed forecasting in the context of sustainable energy planning.
The online version contains supplementary material available at 10.1038/s41598-025-10641-7.
准确的风速预测在支持可再生能源系统、提高飞行安全和增强天气预报方面发挥着关键作用。然而，风型的多变性和非平稳性使得可靠的预测成为一项艰巨的任务。为了解决这些问题，本研究提出了一种混合方法，该方法在集成设置中结合了小波变换（WT）分解、编码器-解码器变压器和XGBoost。为了有效地微调模型参数，该方法将混沌台球优化器（CBO）与Adam优化器一起使用。在此框架中，WT有助于将风速信号分解为不同的频带，捕捉短期变化和长期行为。变压器模型专注于学习复杂的基于时间的依赖性，而XGBoost通过减少过拟合和提高泛化能力为最终预测增加稳健性。该模型每小时预测风速值，提前24小时。CBO的使用确保了在最小参数调整的情况下有效收敛，与包括Adam、粒子群优化（PSO）和遗传算法（GA）在内的传统优化器相比，该模型适用于大规模数据集。当在通过Open-Meteo平台从埃及收集的真实风速数据上进行测试时，所提出的模型表现出强大的性能。它的平均绝对误差（MAE）为0.0218，均方误差（MSE）为0.0008，均方根误差（RMSE）为0.0290，R²分数为0.9625，平均绝对百分比误差（MAPE）为11.97%，解释方差分数（EVS）为0.9521。这些结果优于线性回归、支持向量回归（SVR）、长短期记忆网络（LSTM）等模型，甚至优于独立的变压器和XGBoost模型。总体而言，这种混合方法为可持续能源规划背景下的风速预测提供了一种可靠且高效的解决方案。
在线版本包含可在10.1038/s41598-025-10641-7获取的补充材料。
