Fagbenle et nbsp al assessed the wind

Fagbenle et al. [7] assessed the wind Tenovin-6 potential of Maiduguri and Potiskum, Nigeria using 21 years monthly mean wind data at 10 m height and found that Maiduguri was better site for standalone and medium scale wind power development. McIntyre et al. [8] estimated the wind potential of Guelph city in Ontario, Canada and concluded that an array of utility-scale turbines could potentially generate 29% of Guelph\’s 2005 total electricity demand, whereas one consisting of small-scale turbines could achieve 10% of that demand. Nor et al. [9] conducted techno-economic wind resource assessment in equatorial regions of Malaysia and concluded that there exist harness able sites for wind power deployment. Based on long-term (1945–1990) wind data from 19 meteorological stations and wind measurements at the sites, Katinas et al. [10] reported that the 10 km wide coastal strip near the Baltic Sea was the most suitable region for large scale wind farm development. Nordman [11] reported that around 29% of Kenya\’s tea factories could utilize the wind power potential to meet their power requirements. Weekes et al. [12] investigated a measure–correlate–predict (MCP) approach based on the bivariate Weibull (BW) probability distribution of wind speeds at pairs of correlated sites. Using the artificial wind data, the BW approach outperformed the regression approaches for all measurement periods. When applied to the real wind speed observations however, the performance of the BW approach was comparable to the regression approaches when using a full 12 month measurement period and generally worse than the regression approaches for shorter data periods.