Historical wind speed dataset of meteorological mast station in Khartoum
The data demonstration article presented here showcases three months of wind speed field records for Khartoum city from June to August 2017. These records were obtained from the SOBA-D161094 meteorological mast station, located within the premises of the National Energy Research Center of Sudan. Usi...
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Veröffentlicht in: | Data in brief 2024-12, Vol.57, p.111115, Article 111115 |
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Sprache: | eng |
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Zusammenfassung: | The data demonstration article presented here showcases three months of wind speed field records for Khartoum city from June to August 2017. These records were obtained from the SOBA-D161094 meteorological mast station, located within the premises of the National Energy Research Center of Sudan. Using the two-parameter Weibull distribution, the scale and shape parameters estimated by the method of moments for this dataset were 4.175 m/s and 2.099, respectively, with a coefficient of determination of 0.975, as provided in the associated literature. The accuracy of the data was verified using spatial wind speed information from the MERRA-2 database, compiled by a NASA observation satellite, with a root mean square error between the ground and remote sensing datasets found to be 0.385. Additionally, the Kolmogorov-Smirnov test suggests that the two samples are drawn from the same population and statistical distribution. Based on the Weibull density function, the mean power transported by wind and the maximum mean power that can be extracted by the turbine were 103.45 W and 61.3 W, respectively. The primary objective of this work is to provide the data in a format that enables its use as a benchmark or for reuse in various research endeavors. Special emphasis is placed on facilitating studies related to the parameter estimation of wind speed statistical distribution models. This approach is akin to the utilization of the RTC France solar cell dataset, which is commonly employed for parameter extraction in equivalent circuit models. The added value of this data lies in its potential to provide information that could reveal unrecognized opportunities for the domestic generation of wind power. |
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ISSN: | 2352-3409 2352-3409 |
DOI: | 10.1016/j.dib.2024.111115 |