Abstract:
Solar radiation is a renewable source of energy that is readily available in the
tropics almost throughout the year. Efficiency of solar energy technologies in food
preservation depends on accurate prediction of irradiation, design and sizing of
solar technology. This study investigated global solar predictive models, modified,
validated and compared five models, for prediction of monthly daily mean solar
radiation in four different locations in Kenya representing four major climatic
conditions. The input variables to the models were; latitude, day length, sunshine
hours, relative sunshine hours, temperature, and precipitation. Solar radiation
data from 2000 to 2014 was used to obtain the monthly daily mean global solar
radiation, to analyze, validate and compare performance of the models. Predicted
and measured data was simulated using MATLAB. Statistical indicators, MBE,
RMSE, t-test and R, were performed to determine the model’s performance.
Results showed that sunshine hours-based models predicted global solar radiation
with higher accuracy in wet and cold, wet and warm climatic conditions, while the
temperature and precipitation models were accurate in solar radiation prediction
in hot and dry climatic conditions. Different solar predicting models should be
applied in varying climatic regions, for accurate prediction of solar irradiation and
in designing of efficient solar energy technologies for specific sites.
