Artificial Neural Network Parameter Optimization: Improving Meteorological Data Predictions through Machine Learning

This study aims to create an artificial neural network (ANN) based model to predict solar irradiance using open-sourced meteorological data. A neural network that is feed-forward with backpropagation was employed to build the model. A large combination of model parameters including learning algorithms, transfer functions, number of hidden layers, and neurons was used to customize the neural network. The data used in this study is a part of the publicly available dataset containing real outdoor measurements provided by The National Renewable Energy Laboratory (NREL). The proposed model has been validated by measuring prediction errors using normalized mean squared error (NMSE) and prediction accuracies using regression value (R). The lowest value of the NMSE error was obtained with a neural network model based on three hidden layers employing 40, 8, and 5 neurons respectively. The R-value of this model was the highest among all models. The results have shown that the ascending/descending distribution of neurons in hidden layers is an important factor among other parameters.