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• W4304690694 abstract "Statistical distribution approaches have been developed to describe wind data due to the intermittent and unpredictable nature of wind speed. The Weibull distribution with two parameters is thought to be the most accurate distribution for modeling wind data. This study seeks wind energy assessment via searching for the optimal estimation of the Weibull parameters. For this target, analytical and heuristic methods are investigated. The analytical methods involve the maximum likelihood, moment, energy pattern factor, and empirical methods, while the heuristic optimization algorithms include particle warm optimization and the Aquila optimizer (AO). Both analytical and heuristic methods are assessed together to fit the probability density function of wind data. In addition, nine models are submitted to find the most appropriate model to represent wind energy production. The error between actual and estimated wind energy density is computed to the model for each study site which has less error of energy. The fit test is performed with real data for the Zafarana and Shark El-Ouinate sites in Egypt for a year. Additionally, different indicators of fitness properties are assessed, such as the root mean square error, determination coefficient (R2), mean absolute error, and wind production deviation. The simulation results declare that the proposed AO optimization algorithm offers greater accuracy than several optimization algorithms in the literature for estimating the Weibull parameters. Furthermore, statistical analysis of the compared methods demonstrates the high stability of the AO algorithm. Thus, the proposed AO has greater accuracy and more stability in the obtained outcomes for Weibull parameters and wind energy calculations." @default.
• W4304690694 created "2022-10-12" @default.
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• W4304690694 date "2022-09-30" @default.
• W4304690694 modified "2023-09-26" @default.
• W4304690694 title "Aquila Optimization Algorithm for Wind Energy Potential Assessment Relying on Weibull Parameters Estimation" @default.
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• W4304690694 doi "https://doi.org/10.3390/wind2040033" @default.
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