Department of Communications and Electronics, Delta Higher Institute of Engineering and Technology, Mansoura, Egypt.
Centre for Artificial Intelligence Research and Optimization, Torrens University Australia, Fortitude Valley, QLD, Australia.
PLoS One. 2023 Feb 7;18(2):e0278491. doi: 10.1371/journal.pone.0278491. eCollection 2023.
Technology for anticipating wind speed can improve the safety and stability of power networks with heavy wind penetration. Due to the unpredictability and instability of the wind, it is challenging to accurately forecast wind power and speed. Several approaches have been developed to improve this accuracy based on processing time series data. This work proposes a method for predicting wind speed with high accuracy based on a novel weighted ensemble model. The weight values in the proposed model are optimized using an adaptive dynamic grey wolf-dipper throated optimization (ADGWDTO) algorithm. The original GWO algorithm is redesigned to emulate the dynamic group-based cooperative to address the difficulty of establishing the balance between exploration and exploitation. Quick bowing movements and a white breast, which distinguish the dipper throated birds hunting method, are employed to improve the proposed algorithm exploration capability. The proposed ADGWDTO algorithm optimizes the hyperparameters of the multi-layer perceptron (MLP), K-nearest regressor (KNR), and Long Short-Term Memory (LSTM) regression models. A dataset from Kaggle entitled Global Energy Forecasting Competition 2012 is employed to assess the proposed algorithm. The findings confirm that the proposed ADGWDTO algorithm outperforms the literature's state-of-the-art wind speed forecasting algorithms. The proposed binary ADGWDTO algorithm achieved average fitness of 0.9209 with a standard deviation fitness of 0.7432 for feature selection, and the proposed weighted optimized ensemble model (Ensemble using ADGWDTO) achieved a root mean square error of 0.0035 compared to state-of-the-art algorithms. The proposed algorithm's stability and robustness are confirmed by statistical analysis of several tests, such as one-way analysis of variance (ANOVA) and Wilcoxon's rank-sum.
基于时间序列数据处理的风速预测技术可以提高高渗透率风力电网的安全性和稳定性。由于风的不可预测性和不稳定性，准确预测风力和风速具有挑战性。已经开发了几种方法来提高这一准确性。这项工作提出了一种基于新型加权集成模型的高精度风速预测方法。所提出的模型中的权重值使用自适应动态灰狼-海豚优化算法（ADGWDTO）进行优化。对原始 GWO 算法进行重新设计，以模拟基于动态群组的协作，以解决在探索和利用之间建立平衡的困难。快速鞠躬和白色胸部，这是海豚喉咙鸟类狩猎方法的特征，用于提高所提出算法的探索能力。所提出的 ADGWDTO 算法优化了多层感知机（MLP）、K-最近回归（KNR）和长短期记忆（LSTM）回归模型的超参数。使用 Kaggle 上名为“2012 年全球能源预测竞赛”的数据集来评估所提出的算法。研究结果证实，所提出的 ADGWDTO 算法优于文献中的风速预测算法。所提出的二进制 ADGWDTO 算法在特征选择方面的平均适应度为 0.9209，标准偏差适应度为 0.7432，所提出的加权优化集成模型（使用 ADGWDTO 的集成）的均方根误差为 0.0035，优于现有算法。通过单向方差分析（ANOVA）和 Wilcoxon 秩和检验等几种测试的统计分析，验证了所提出算法的稳定性和鲁棒性。
