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

Abstract

Long 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 (PM 10 and PM 2.5 ) 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 (R 2 ) and Mean Absolute Percentage Error (MAPE) are used for accuracy evaluation of modeling results.

Regression Model to Predict Global Solar Irradiance in Malaysia

“…The developed model could observe the effect of the time step on the accuracy of solar irradiance on an hourly, daily, and monthly basis. Vakili et al [24] utilized an artificial neural network (ANN) method to estimate global solar irradiance. Parameters, such as particulate matter, were used as inputs along with temperature, relative humidity, and wind speed.…”

Mathematical and neural network models for predicting the electrical performance of a PV/T system

“…Simulations attempt to describe and discuss the characteristics and the behavior of any physical system using a simulation system that emulates the functioning of the real system under artificial conditions. Many researchers used similar system modeling using artificial neural network (ANN); in Table , some of the most important of these studies are listed …”

Deep learning and regression modelling of cloudless downward longwave radiation

“…Expert systems like neural networks (NN) are reputed for better performance than traditional models [41,72]. To this end, NN has been used to model global solar radiation to meet the data needs of thermal and photovoltaic applications [32,37,77,103,119]. Compared with traditional methods, NN often deploys more atmospheric parameter inputs [75,93].…”