Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 1034
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3152
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
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 (R), and chi-square test (X), 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.
Download full-text PDF |
Source |
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http://dx.doi.org/10.1007/s11356-023-25689-z | DOI Listing |
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