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Article history: Received: .08 May, 2020 Accepted: 07 August, 2020 Online: 19 August, 2020 Estimation Solar radiation is the most significant part of the optimization of solar power. This may be achieved if the solar radiation is predicted well in advance. Meteorological stations have radiation measuring devices like pyranometer, pyrheliometer, radiometer, solarimeter, etc. however, it may not be available at the location of interest for researchers. Due to this limitation solar radiation estimation models are devised based on location details like Altitude, Latitude, Longitude, and metrological details like Wind Speed, Ambient Temperature, Relative Humidity, Day Temperature, etc. These radiation models provide Global Solar Radiation (GSR) as output. These models are statistically tested based on error calculation like Mean Bias Error, Mean Absolute Error, Root Mean Square Error, etc. This paper is framed to briefly provide the idea behind different solar radiation estimation models with the methodology used. Soft computing-based models are mainly analyzed here. ANN-based Global Solar Irradiance Estimation Model has been developed using geographical parameters like Elevation, Latitude, Longitude, Longitude, and meteorological parameters like Months of a year, Days of a month, Temperature, Atmospheric Pressure, Relative Humidity, and Wind Speed. The data are downloaded from the National Solar Radiation Database (NSRDB) for 2014 (latest available). From this paper, the reader will come to know about various techniques used in solar radiation estimation. The developed ANN-based model has better results for training, testing, validation, and all with Regression value of 0.94304, 0.9488, 0.94766, 0.94556 respectively. The MSE is found to be 0.0089147 at epoch 0. The obtained values of R and MSE indicates the suitability of the developed model.

Overview of Solar Radiation Estimation Techniques with Development of Solar Radiation Model Using Artificial Neural Network