부산대학교 도서관

In this work, we conduct a comparative analysis of Machine Learning (ML) and Deep Learning (DL) approaches for solar irradiation forecasting at Batna station. Specifically, we examine six different ML methods: Linear Regression (LR), Multilayer Perceptron (MLP), Extreme Learning Machine (ELM), Kernel Ridge Regression (KRR), Support Vector Regression (SVR), and Gaussian Process Regression (GPR). Furthermore, we investigate a deep learning-based regression approach considered a relatively recent field technique. We propose to use three DL methods, namely Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and Bi-directional Long Short-Term Memory (Bi-LSTM). We present and discuss the experimental outcomes obtained from the Batna station data.