Bommidi Bala Saibabu, Teeparthi Kiran, Dulla Mallesham Vinod Kumar
Department of Electrical Engineering, National Institute of Technology Andhra Pradesh, Tadepalligudem, 534101, Andhra Pradesh, India.
Department of Electrical and Electronics Engineering, Prasad V Potluri Siddhartha Institute of Technology, Vijayawada, 520007, Andhra Pradesh, India.
Environ Sci Pollut Res Int. 2024 May;31(23):34056-34081. doi: 10.1007/s11356-024-33383-x. Epub 2024 May 2.
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.
为应对紧迫的全球环境挑战和不断增长的能源需求，人们对风能技术进行了广泛而深入的研究。准确的风速预测对于大型风力发电系统的有效整合至关重要。本研究提出了一种名为ICEEMDAN-Informer-GWO的新型混合框架，该框架结合了三个组件以提高风速预测的准确性。改进的自适应噪声完备总体经验模态分解（ICEEMDAN）组件改善了风速数据的分解，Informer模型提供了计算效率高的风速预测，灰狼优化（GWO）算法对Informer模型的参数进行优化以实现卓越性能。使用来自布洛克岛、墨西哥湾沿岸和花园城的三组不同的风速预测（WSP）模型和风力发电场数据，对所提出的混合框架进行了全面评估。该评估聚焦于提前5分钟、30分钟和1小时这三个特定时间范围的风速预测。从所进行的三个实验中获得的结果确凿地表明，所提出 的混合框架表现出卓越性能，在所有三个时间范围内都带来了具有统计学意义的显著改进。
