Abstract
Background. Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Consequently, countries are being forced to seek potential alternative sources of energy such as wind, solar, and photovoltaic among many others. However, the realization of their benefits is faced with challenges. Though wind stands a chance to solve this problem, the lack of adequate site profiles, long-term behavioural information, and specific data information that enables informed choice on site selection, turbine selection, and expected power output has remained a challenge to its exploitation. In this research, Weibull and Rayleigh models are adopted. Wind speeds were analyzed and characterized in the short term and then simulated for a long-term measured hourly series data of daily wind speeds at a height of 10 m. The analysis included daily wind data which was grouped into discrete data and then calculated to represent the mean wind speed, diurnal variations, daily variations, and monthly variations. To verify the models, statistical tools of Chi square, RMSE, MBE, and correlational coefficient were applied. Also, the method of measure, correlate, and predict was adopted to check for the reliability of the data used. The wind speed frequency distribution at the height of 10 m was found to be 2.9 ms-1 with a standard deviation of 1.5. From the six months’ experiments, averages of wind speeds at hub heights of 10 m were calculated and found to be 1.7 m/s, 2.4 m/s, and 1.3 m/s, for Ikobe, Kisii University, and Nyamecheo stations, respectively. The wind power density of the region was found to be 29 W/m2. By a narrow margin, Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Wind speeds at the site are noted to be decreasing over the years. The region is shown as marginal on extrapolation to 30 m for wind energy generation hence adequate for nongrid connected electrical and mechanical applications. The strong correlation between the site wind profiles proves data reliability. The gradual decrease of wind power over the years calls for attention.
Highlights
Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel
This study was carried out based on data obtained from the Kenya meteorological department station located in Kisi town at KARL which lies along the equator in the western parts of Kenya backed with data obtained from three selected sites of Kisii University (KSU), Nyamecheo, and Ikobe of Kisii region
The Kisii site shows a different pattern with prevailing winds appearing approximately between 0300 hours and 0800 hours. This implies that for the stations of Ikobe and Nyamecheo, the wind speed is high during the midday approaching evenings when the temperature is high, while on the other hand, wind speed is high after midnight approaching dawn for the Kisii station when the temperature is low and in a reducing trend
Summary
Global warming is a growing threat in the world today mainly due to the emission of CO2 caused by the burning of fossil fuel. Rayleigh proves to be a better method over Weibull in predicting wind power density in the region. Due to inadequate access to energy and the impact of climate changes associated with it, sub-Saharan Africa still remains considered the most poorly connected to electricity region around the world with more than 6 million of its population lacking access to this basic want [1]. According to the International Energy Agency, 2014, the demand for electricity in Africa is estimated to grow by 4% each year till 2040. This implies that sub-Saharan Africa needs to expand significantly its installed energy capacity as well as upgrade its power grids to meet this increasing demand. It can be shown that more than 75% of the population in sub-Saharan Africa is still not connected to electricity
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