A comprehensive study and performance analysis of deep neural network-based approaches in wind time-series forecasting

• المؤلفون الرئيسيون: Rahman M.M., Shakeri M., Khatun F., Tiong S.K., Alkahtani A.A., Samsudin N.A., Amin N., Pasupuleti J., Hasan M.K.
• مؤلفون آخرون: 58831072700
• التنسيق: مقال
• منشور في: Springer Science and Business Media Deutschland GmbH 2024
• الموضوعات: NARX neural network; Recurrent neural network; Renewable energy; Time-series forecasting; Wind-speed prediction; Curve fitting; Deep neural networks; Errors; Gas emissions; Global warming; Greenhouse gases; Mean square error; Recurrent neural networks; Time series; Time series analysis; Weather forecasting; Wind speed; Forecasting: applications; Network-based approach; Neural network model; Performance; Prediction modelling; Renewable energies; Time series forecasting; Wind speed prediction; Wind time series; Neural network models

The increasing energy demand and expansion of power plants are provoking the effects of greenhouse gas emissions and global warming. To mitigate these issues, renewable energies (like solar, wind, and hydropower) are blessings for modern energy sectors. The study focuses on wind-speed prediction in...