Global solar radiation estimation model with two parameters and its ANN validation

In this study the usability of routinely measured meteorological parameters to estimate the global solar radiation is investigated. The proposed models are in the form of polynomials. The parameters such as ratio of duration of sunshine to maximum sunshine hours, mean temperature and mean relative humidity are used. Different combinations of these parameter sets have been used in proposing the monthly estimation models with least errors. 2 parameter model with parameters maximum temperature and mean relative humidity exhibits the best match with least RMSE´s within 0.185 between estimated and measured values of global solar radiation. Based on the overall results, it is concluded that, temperature and humidity make a better combination in estimating the global solar radiation as compared with the conventional sunshine duration parameter. Using these commonly available meteorological parameters such as air temperature, relative humidity, wind speed and moisture, Iranna-Bapat models fairly estimate the Global Solar Radiation at any location on the earth surface. The proposed models have been validated with Artificial Neural Networks, for the data from 875 stations around the world.