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. 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.

Authors

Institutions

Publication Details

Journal
Data in Brief
Published
2024-11-07
DOI
https://doi.org/10.1016/j.dib.2024.111115
Citations
1
Primary Topic
Aerodynamics and Fluid Dynamics Research
Type
article
Field-Weighted Citation Impact
1.30

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Historical wind speed dataset of meteorological mast station in Khartoum

Hazim Elshiekh, Ali Omer, Yassir Yassin, Abubaker Younis et al.
1 citations
Data in Brief
Aerodynamics and Fluid Dynamics Research
1.30
article

Historical wind speed dataset of meteorological mast station in Khartoum

Hazim Elshiekh, Ali Omer, Yassir Yassin, Abubaker Younis, Elfadil Biraima
article en
1 citations

Abstract

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.

Data in BriefVol. 57
National Centre for Research (Sudan) (SD)
Universitatea Transilvania din Brasov
Affordable and clean energy, Climate action
Openalex Percentile: Top 12%
Aerodynamics and Fluid Dynamics Research
1.30
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.