Alharthi Yahya Z, Chiroma Haruna, Gabralla Lubna A
Department of Electrical Engineering, College of Engineering, University of Hafr Albatin, 39524, Hafr Al Batin, Saudi Arabia.
College of Computer Science and Engineering, Applied College, University of Hafr Al Batin, Hafr Al Batin, Saudi Arabia.
Sci Rep. 2025 May 8;15(1):16119. doi: 10.1038/s41598-025-98212-8.
The increasing global interest in utilizing wind turbines for power generation emphasizes the importance of accurate wind power forecasting in managing wind power. This paper proposed a framework that integrates a data transformation mechanism with a multi-objective none-dominated sorting genetic algorithm III (NSGA-III), coupled with a hybrid deep Recurrent Network (DRN) and Long Short-Term Memory (LSTM) architecture for modeling wind power. The feature selection algorithm, multi-objective NSGA-III, identifies the optimal subset features from wind energy datasets. These selected features undergo a data transformation process before being input into the hybrid DRN-LSTM for wind power forecasting. A comparative study demonstrates the proposal's superior effectiveness and robustness compared to existing frameworks with the proposal achieving 2.6593e-10 and 1.630e-05 in terms of MSE and RMSE respectively whereas the classical algorithm recorded 8.8814e-07 and 9.424e-04. The study's contributions lie in its approach integration of data transformation mechanism and the notable enhancements in wind power forecasting accuracy. Furthermore, the study offers valuable insights to guide research efforts in the future.
全球对利用风力涡轮机发电的兴趣日益浓厚，这凸显了准确的风电功率预测在风电管理中的重要性。本文提出了一个框架，该框架将数据转换机制与多目标非支配排序遗传算法III（NSGA-III）相结合，并结合了一种混合深度循环网络（DRN）和长短期记忆（LSTM）架构来对风电功率进行建模。特征选择算法多目标NSGA-III从风能数据集中识别出最优子集特征。这些选定的特征在输入到用于风电功率预测的混合DRN-LSTM之前要经过数据转换过程。一项对比研究表明，与现有框架相比，该提议具有更高的有效性和鲁棒性，该提议在均方误差（MSE）和均方根误差（RMSE）方面分别达到了2.6593e-10和1.630e-05，而经典算法的记录分别为8.8814e-07和9.424e-04。该研究的贡献在于其数据转换机制的方法整合以及风电功率预测准确性的显著提高。此外，该研究为未来的研究工作提供了有价值的见解。
