In Environmental science and pollution research international
This study uses novel evolutionary algorithms and computational techniques to analyze wind potential on flat, complex coastal, and offshore sites utilizing mast as well as remote sensing data. The wind data were recorded using remote sensing technique and conventional technique. The optimum Weibull parameters are estimated using nine methods. The genetic algorithm, particle swarm optimization, and TLBO algorithms are compared and evaluated. The goodness of fit test, such as root mean square error test (RMSE), mean absolute percentage error (MAPE), coefficient of determination (R2), and chi-square test (X2), is used to evaluate the accuracy of the selected methods. Parameter estimates are used to compute wind densities. The TLBO and PSO algorithms outperformed genetic algorithms in terms of efficiency. This research compares remote sensing measurements to cup anemometer measurements.
Shende Vikas, Patidar Harsh, Baredar Prashant, Agrawal Meena
2023-Feb-10
Machine learning, Offshore wind energy, Probability distribution function, Statistical analysis, Wind speed
