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pro vyhledávání: '"Amir Abdul Majid"'
Autor:
Amir Abdul Majid
Publikováno v:
Frontiers in Energy Research, Vol 11 (2023)
This work aims to evaluate different error estimations of the shape and scale parameters of the Weibull probability density function of wind speed measured at the Fujairah site over a 1-year period. This study estimates trends in the variation of Wei
Externí odkaz:
https://doaj.org/article/be9651a46838447d913b447a120cc3e8
Autor:
Amir Abdul Majid
Publikováno v:
Energy Conversion and Management: X, Vol 16, Iss , Pp 100286- (2022)
The aim of this work is to find the most efficient and suitable input features to be selected for forecasting monthly wind energy accurately. Machine learning is employed for a modular pipelined neural network, composed of time-delayed and feedforwar
Externí odkaz:
https://doaj.org/article/5f605ce5b7854dd6abcaf66c09188ea2
Autor:
Amir Abdul Majid
Publikováno v:
Energies, Vol 16, Iss 12, p 4766 (2023)
This study aims to focus on using the Volterra series and machine learning for forecasting random and chaotic wind speed regimes, since calm weather is mostly noticed at the local site, making dataset selection difficult. A novel method is proposed t
Externí odkaz:
https://doaj.org/article/e495bef5f4264c1b9f87cf40fe2bedd7
Autor:
Amir Abdul Majid
Publikováno v:
Energies, Vol 15, Iss 22, p 8596 (2022)
The aim of this research was to forecast monthly wind energy based on wind speed measurements that have been logged over a one-year period. The curve type fitting of five similar probability distribution functions (PDF, pdf), namely Weibull, Exponent
Externí odkaz:
https://doaj.org/article/c9faa339b70f46f6b62eac61d7830c62