Abstract：
Extensive research has been diligently conducted on wind energy technologies in response to pressing global environmental challenges and the growing demand for energy. Accurate wind speed predictions are crucial for the effective integration of large wind power systems. This study presents a novel and hybrid framework called ICEEMDAN-Informer-GWO, which combines three components to enhance the accuracy of wind speed predictions. The improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) component improves the decomposition of wind speed data, the Informer model provides computationally efficient wind speed predictions, and the grey wolf optimisation (GWO) algorithm optimises the parameters of the Informer model to achieve superior performance. Three different sets of wind speed prediction (WSP) models and wind farm data from Block Island, Gulf Coast, and Garden City are used to thoroughly assess the proposed hybrid framework. This evaluation focusses on WSP for three specific time horizons: 5 minutes, 30 minutes, and 1 hour ahead. The results obtained from the three conducted experiments conclusively demonstrate that the proposed hybrid framework exhibits superior performance, leading to statistically significant improvements across all three time horizons.
摘要：
为了应对紧迫的全球环境挑战和日益增长的能源需求，人们对风能技术进行了广泛的研究。准确的风速预测对于大型风力发电系统的有效集成至关重要。本研究提出了一种新颖的混合框架，称为ICEEMDAN-Informer-GWO，它结合了三个组件，以提高风速预测的准确性。改进的带自适应噪声的完整集成经验模式分解(ICEEMDAN)分量改进了风速数据的分解,Informer模型提供了计算有效的风速预测，灰狼优化（GWO）算法对Informer模型的参数进行了优化，以实现卓越的性能。来自布洛克岛的三组不同的风速预测（WSP）模型和风电场数据，墨西哥湾沿岸,和花园城市被用来彻底评估拟议的混合框架。此评估集中在三个特定时间范围内的WSP：5分钟，30分钟,提前1小时。从三个进行的实验中获得的结果最终证明了所提出的混合框架具有优越的性能，导致在所有三个时间范围内的统计显着改善。
