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Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting
Engineering with Computers ( IF 8.7 ) Pub Date : 2021-03-08 , DOI: 10.1007/s00366-021-01356-0
Seyed Mohammad Jafar Jalali , Sajad Ahmadian , Mahdi Khodayar , Abbas Khosravi , Vahid Ghasemi , Miadreza Shafie-khah , Saeid Nahavandi , João P. S. Catalão

High accurate wind speed forecasting plays an important role in ensuring the sustainability of wind power utilization. Although deep neural networks (DNNs) have been recently applied to wind time-series datasets, their maximum performance largely leans on their designed architecture. By the current state-of-the-art DNNs, their architectures are mainly configured in manual way, which is a time-consuming task. Thus, it is difficult and frustrating for regular users who do not have comprehensive experience in DNNs to design their optimal architectures to forecast problems of interest. This paper proposes a novel framework to optimize the hyperparameters and architecture of DNNs used for wind speed forecasting. Thus, we introduce a novel enhanced version of the grasshopper optimization algorithm called EGOA to optimize the deep long short-term memory (LSTM) neural network architecture, which optimally evolves four of its key hyperparameters. For designing the enhanced version of GOA, the chaotic theory and levy flight strategies are applied to make an efficient balance between the exploitation and exploration phases of the GOA. Moreover, the mutual information (MI) feature selection algorithm is utilized to select more correlated and effective historical wind speed time series features. The proposed model’s performance is comprehensively evaluated on two datasets gathered from the wind stations located in the United States (US) for two forecasting horizons of the next 30-min and 1-h ahead. The experimental results reveal that the proposed model achieves the best forecasting performance compared to seven prominent classical and state-of-the-art forecasting algorithms.



中文翻译:

面向新型深层神经进化模型:混沌风草优化,用于短期风速预测

高精度的风速预测在确保风能利用的可持续性方面发挥着重要作用。尽管深度神经网络(DNN)最近已应用于风时间序列数据集,但其最大性能很大程度上取决于其设计的体系结构。通过当前最新的DNN,它们的体系结构主要以手动方式配置,这是一项耗时的任务。因此,对于在DNN中没有全面经验的普通用户来说,设计其最佳体系结构以预测感兴趣的问题是困难而令人沮丧的。本文提出了一种新的框架,用于优化用于风速预测的DNN的超参数和体系结构。因此,我们介绍了一种称为EGOA的蚱optimization优化算法的新型增强版本,以优化深长短期记忆(LSTM)神经网络体系结构,从而优化了其四个关键超参数的演化。为了设计GOA的增强版本,应用了混沌理论和征税飞行策略,以在GOA的开发和勘探阶段之间实现有效的平衡。此外,互信息(MI)特征选择算法用于选择更相关和有效的历史风速时间序列特征。在从位于美国(US)的风电场收集的两个数据集上,对未来30分钟和1小时之前的两个预测范围进行了综合评估,对所提出模型的性能进行了评估。

更新日期:2021-03-08
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