Using Artificial Neural Networks for Prediction of Global Solar Radiation in Tehran Considering Particulate Matter Air Pollution

Abstract

AbstractLong term measurements of the amount of solar energy at ground level are not easily possible in many locations. Therefore, using empirical relations and recently applying Artificial Neural Networks (ANN) are common means for prediction of the available solar energy at desired areas. Recent studies indicate that the performance of ANN provides better prediction than empirical relations. In former researches about ANN modeling of solar energy for some geographical locations, the parameters such as maximum and minimum daily temperature, relative humidity and wind speed were considered as the input of the soft computing. In present Multilayer Perceptron (MLP) ANN modeling, the amount of suspended Particulate Matters (PM10 and PM2.5) in the atmosphere is also added to the soft computation input. This ANN modeling strategy is used for estimating the amount of daily absorption of global solar radiation (both beam and diffuse radiation) on the land surface of Tehran (Longitude 51.23N and Latitude 35.44E) during a year. Furthermore, Indexes of Root Mean Square Error (RMSE), Absolute Fraction of Variance (R2) and Mean Absolute Percentage Error (MAPE) are used for accuracy evaluation of modeling results