Taha Ali, Nazih Nathalie, Makeen Peter
Electrical Engineering Department, Faculty of Engineering, The British University in Egypt (BUE), Al Shorouk City, Egypt.
Sci Rep. 2025 May 4;15(1):15599. doi: 10.1038/s41598-025-98543-6.
Wind energy has become a key answer to the world's energy problems, providing a clean and sustainable option instead of relying on fossil fuels. Enhancing wind energy systems and energy management is essential through efficient wind speed prediction. However, the complex nature of wind speed data contains significant challenges with existing forecasting models for long-term nonlinear forecasting accuracy, and this causes a lack of wind energy predictions, which may cause false distributions of energy. This study proposes a multi-step methodology that integrates Variational Mode Decomposition (VMD) with advanced machine learning like Extreme Gradient Boosting (XGBoost), Adaptive Boosting (AdaBoost), Light Gradient Boosting Machine (LightGBM), K-Nearest Neighbor (KNN), and transformer-based model (Informer) to improve long-term wind speed forecasting. The approach involves data collection from the NASA Power project, which consists of 35k samples of wind speed data, with performance evaluated on R-squared (R²) score and error metrics. The proposed approach demonstrated state-of-the-art performance, with LightGBM achieving the highest R² of 98% and the lowest error metrics. XGBoost and KNN performed slightly lower in R², achieving 97% score. Despite the high performance of the Informer model, it demonstrated the lowest in scores with a 78% R² score. The study's novelty lies in highlighting the effectiveness and efficiency of VMD in addressing the complexities of wind speed data and underscores the potential of combining decomposition techniques with advanced machine learning models for accurate wind speed forecasting.
风能已成为解决全球能源问题的关键答案，提供了一种清洁且可持续的选择，而非依赖化石燃料。通过高效的风速预测来增强风能系统和能源管理至关重要。然而，风速数据的复杂特性给现有的长期非线性预测精度的预测模型带来了重大挑战，这导致风能预测不足，可能造成能源的错误分配。本研究提出了一种多步骤方法，将变分模态分解（VMD）与先进的机器学习方法（如极端梯度提升（XGBoost）、自适应提升（AdaBoost）、轻量级梯度提升机（LightGBM）、K近邻（KNN）和基于变压器的模型（Informer））相结合，以改进长期风速预测。该方法涉及从美国国家航空航天局电力项目收集数据，该项目包含35000个风速数据样本，并根据决定系数（R²）得分和误差指标评估性能。所提出的方法展现了最先进的性能，LightGBM达到了最高的R²值98%和最低的误差指标。XGBoost和KNN在R²值上表现稍低，达到了97%的得分。尽管Informer模型性能较高，但它的得分最低，R²值为78%。该研究的新颖之处在于突出了VMD在解决风速数据复杂性方面的有效性和效率，并强调了将分解技术与先进机器学习模型相结合以进行准确风速预测的潜力。
