Solar Radiation Prediction Models Analysis for Varying Climatic Conditions

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.