Abstract
This study analyses the predictability of solar electricity generation using various machine and deep learning methods on large solar datasets from diverse cities in Saudi Arabia and the United States. According to our most recent article [1], the "Multilayer Perceptron" and "Random Forest" algorithms perform better in forecasting Saudi Arabia's solar power generation. This finding has been validated using additional datasets in the present study. Additionally, the effects of various hidden layer and neuron number combinations on MLP performance are examined. We found beyond a certain point, the number of hidden layers in an MLP became inversely correlated with its prediction accuracy. As the number of neurons in the model increases, the training duration also increases, generally improving predictability. The RMSE of deep learning algorithms such as the feedforward neural network (FFNN), convolutional neural network (CNN), and long short-term memory (LSTM) are compared against the MLP and Random Forest to evaluate their feasibility in estimating solar power generation. We found that FFNN and MLP provide almost similar results and Random Forest gives the best results among all the ML and DL algorithms for predicting solar power generation using our datasets. Future work may explore different aspects of the Random Forest model.