July 22, 2024
Vahid Ghasemi

Vahid Ghasemi

Academic rank: Assistant professor
Address: -
Education: Ph.D in Computer Engineering
Phone: 08338305001-(1108 داخلی )
Faculty: Faculty of Information Technology


Towards novel deep neuroevolution models: chaotic levy grasshopper optimization for short-term wind speed forecasting
Type Article
Wind speed forecasting; Deep neuroevolution; Long short-term memory; Enhanced grasshopper optimization algorithm
Researchers Seyed Mohammad Jafar Jalali، Sajad Ahmadian، mahdi khodayar، Abbas Khosravi، Vahid Ghasemi، Miadreza Shafie-khah، Saeid Nahavandi، Joao Catalao


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.