Abstract
Wind speed modelling has been key to many environmental and engineering applications, particularly in environmentally friendly wind power generation to meet energy demands. Efficient assessment of wind speed at different recurrence intervals requires the choice of a suitable statistical distribution and an unbiased method of parameter estimation. This study suggests the use of a four parameter Kappa distribution, with its parameters estimated using the method of L-moments, to model Botswana’s monthly maximum wind speed data at six meteorological weather stations. These synoptic weather stations are Gaborone, Sir Seretse Khama Airport, Tsabong, Tshane, Gantsi and Maun which are broadly spread across the country’s economic activity centres. Reliable wind speed quantiles have been obtained for the selected stations and have been found to fall within the interval 13.80 to 21.69 m s-1. Mean maximum wind speeds have been found to range between 12.65 and 14.97 m s-1, with standard deviations ranging between 1.58 and 2.44 m s-1. These results can reliably be used by environmentalists and technologists working in the energy sector in Botswana.
Highlights
Wind speed is an important factor in many sectors
The estimated parameters of the 4-P Kappa distribution were substituted into Equation 2 to obtain estimates of wind speed quantiles (Park et al, 2001) for each station, corresponding to the recurrence intervals (T):10, 20, 50, 100, 200 and 500 years
Maximum wind speed data for Gaborone, Sir Seretse Khama Airport, Tsabong, Tshane, Gantsi and Maun have been modelled using a four parameter Kappa distribution based on L-moment procedure, which has made it possible to obtain reliable wind speed quantiles at recurrence intervals 10, 20, 50, 100, 200 and 500 years
Summary
Wind speed is an important factor in many sectors It is an important parameter in the estimation of evapotranspiration for agricultural purposes (Valipour and Eslamian, 2014; Valipour, 2014a; 2014b; 2015a; 2015b; 2015c; 2015d). A four parameter Kappa distribution with its parameters estimated using the L-moments method has been used to model Botswana's monthly maximum wind speeds. This distribution encompasses a family of distributions that can effectively describe the peculiarity in data variability (Hosking, 1986; 1990; 1994; Parida, 1999; Shabri and Jemain, 2010). Designers and practicing engineers could make use of this information to plan and produce wind power efficiently (Chiodo, 2013; Mukhopadhyay et al, 2014)
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