Prediction of monthly mean daily global solar radiation using Artificial Neural Network

Journal of Earth system science

R Samuel Selvaraj2²  V Sivamadhavi1¹ 

[1] Department of Physics, Bharathi Women’s College, Chennai, Tamil Nadu, India.$$;Department of Physics, Presidency College, Chennai, Tamil Nadu, India.$$

关键词: Artificial neural network;    back propagation;    global radiation;    multilayer perceptron network;    Tamil Nadu.;   

DOI  :  

学科分类：天文学(综合)

来源: Indian Academy of Sciences

【 摘 要 】

In this study, a multilayer feed forward (MLFF) neural network based on back propagation algorithm was developed, trained, and tested to predict monthly mean daily global radiation in Tamil Nadu, India. Various geographical, solar and meteorological parameters of three different locations with diverse climatic conditions were used as input parameters. Out of 565 available data, 530 were used for training and the rest were used for testing the artificial neural network (ANN). A 3-layer and a 4-layer MLFF networks were developed and the performance of the developed models was evaluated based on mean bias error, mean absolute percentage error, root mean squared error and Student’s 𝑡-test. The 3-layer MLFF network developed in this study did not give uniform results for the three chosen locations. Hence, a 4-layer MLFF network was developed and the average value of the mean absolute percentage error was found to be 5.47%. Values of global radiation obtained using the model were in excellent agreement with measured values. Results of this study show that the designed ANN model can be used to estimate monthly mean daily global radiation of any place in Tamil Nadu where measured global radiation data are not available.

【 授权许可】

Unknown