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

Abstract

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.

Investigation of Seasonal and Annual Wind Speed Distribution of Tarnab Based on Weibull and Rayleigh Distribution Models

“…Originally theorized by physicist Waloddi Weibull, the Weibull distribution (Rajput et al, 2022) is a statistical distribution with numerous applications in meteorological studies and software applications (such as Wind Atlas Analysis and Application Program, WasP) based on its mathematical framework (Younis et al, 2023). Other distributions for modeling wind speed may also be utilized, but the Weibull distribution has the edge over them as it can model both right-and left-skewed wind speeds, which other models fall short of (Rajput et al, 2022).…”

Utilizing the Honeybees Mating-Inspired Firefly Algorithm to Extract Parameters of the Wind Speed Weibull Model

“…Many applications have relied on the FA to identify the parameters of their relevant models, such as the hybrid model of the solar charging station [65], the capacitated vehicle routing problem [63], civil engineering, meteorological optimization, and other relevant engineering applications [58]. It is worth mentioning that the FA algorithm and its variants are not commonly used in wind energy research, with a few exceptions, such as the work conducted by Younis et al to forecast wind speed based on data from Khartoum, Sudan [66]; and the study by Shan et al, where they developed a hybrid FA variant used to tune the parameters of a variable pitch wind turbine PID controller [52].…”

“…Many applications have relied on the FA to identify the parameters models, such as the hybrid model of the solar charging station [65], the cap routing problem [63], civil engineering, meteorological optimization, and engineering applications [58]. It is worth mentioning that the FA algorithm are not commonly used in wind energy research, with a few exceptions, s conducted by Younis et al to forecast wind speed based on data from Kh [66]; and the study by Shan et al, where they developed a hybrid FA varia the parameters of a variable pitch wind turbine PID controller [52]. The current study utilizes the honeybees mating optimizer (HBMO) tion to calculate a conditional value representing the small proportion o that engage in cannibalism instead of mating.…”

“…As with other metaheuristic algorithms, the initialization of the first generation or population is accomplished using Equation (2) [66,70], as shown in Figure 4 [70]. This is followed by the FA update function given in Equation ( 3), which represents the movement of the i firefly towards j firefly, in which the distance between them (r ij ) is given in Equation ( 4) [71].…”

“…It should be briefly noted here that the primary control parameters in the basic FA, which directly influence the algorithm, include the randomization parameter (α) typically static but with a dynamic version in some FA variants [72]; the light absorption coefficient (γ); and the attractiveness (β), often denoted as β 0 when there is no distance between fireflies (i.e., when r = 0) [50]. Figure 5 provides the pseudo-code of the classic FA [50,66].…”