Stochastic Simulation of Hourly Average Wind Speed in Umudike , South-East Nigeria

The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria

“…The dearth in meteorological data such as wind speed data has led to limited study and hence the exploration of wind energy resources in Nigeria. A few number of studies have been conducted at specific locations [18][19][20][21][22][23][24][25]. In those studies, the wind speed variability was modelled using different analytical tools such as: statistical models including Weibull and Rayleigh distribution functions; stochastic simulation; seasonal autoregressive integrated moving average; linear and multiple regression models.…”

“…In their study, wind data for 22 ground stations from 12 to 33 years (1951-1983) were used to create isovents based on the yearly average values of wind speed. The use of limited number (22) of sampling points and the yearly average values of wind speed underscores the high variability of wind speed in space and time and hence, limits the accuracy and the applicability of the proposed map. To improve the accuracy application of the map, more sampling points and smaller time frame interval (monthly) variability is required.…”

On the Remapping and Identification of Potential Wind Sites in Nigeria

“…The mean wind speed at this station was not economically viable for a medium nor utility-scale energy application but suitable for small-scale application only as the station falls within class 1 of the international system of wind classification. Furthermore, a number of few wind studies have been conducted on assessments of the wind speed characteristics at different locations in Nigeria by Ojosu et al [4] [5], Adekoya et al [6], Anyanwu et al [7], Agbaka [8], Igbokwe et al [9], Medugu et al [10], Ngala et al [11] and Oriaku et al [12]. In literature, the wind speed record was modeled using different analytical tools such as: statistical modeling (Weibull and Rayleigh distribution functions); Seasonal Autoregressive Integrated Moving Average Modeling; Linear and Multiple Regression modeling; stochastic simulation; and Artificial Neural Network (ANN).…”