Historical wind speed dataset of meteorological mast station in KhartoumFigshare

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

Wind Speed Forecast for Sudan Using the Two-Parameter Weibull Distribution: The Case of Khartoum City

In this quick study, we estimated the Weibull distribution’s parameters using wind data collected between March 2017 and January 2018 using a twelve-meter mast meteorological station on the grounds of the National Energy Research Center in Khartoum. In order to quantify these descriptors, we relied on analytical and stochastic methods, subsequently enabling specialists from researchers, engineers, decision-makers, and policymakers to apprehend the wind characteristics in the vicinity. Hence, the computed scale and shape parameters were provided, in which the Firefly algorithm (FA) resulted in the most accuracy in terms of the coefficient of determination, which equaled 0.999, which we considered logical due to the observed nonlinearity in the wind speed numbers. On the contrary, the energy pattern factor method had the worst prediction capability depending on several goodness-of-fit metrics. This concise work is unique because it is the first to use data from Sudan to forecast local wind speeds using artificial intelligence algorithms, particularly the FA technique, which is widely used in solar photovoltaic modeling. Additionally, since classic estimating approaches act differently spatially, evaluating their efficacy becomes innovative, which was accomplished here. On a similar note, a weighted-average wind speed was found to equal 4.98 m/s and the FA average wind speed was 3.73 m/s, while the rose diagram indicated that most winds with potential energy equivalent to 3 m/s or more blow from the north