Harnessing the Sun: Implementation of Statistical, ML and ANN based Models for Solar Forecasting

As a result of the increased use of renewable energy technologies, such as solar and wind power, the development of reliable forecasting methodologies has become necessary in order to maximize the integration of these intermittent and variable resources into the electrical grid. Traditional techniques of forecasting have inherent limitations when it comes to capturing the intricate and nonlinear patterns that are inherent in the generation of renewable energy that are present. On the other hand, the fast improvements in machine learning and deep learning models have opened up new potential for the development of forecasting systems for renewable energy that are more accurate and resilient.The limits of standard forecasting approaches, which frequently struggle to capture the complex non-linear connections and dynamic patterns that are inherent in renewable energy systems, have prompted academics to investigate the application of sophisticated machine learning and deep learning models as a means of addressing these constraints. The purpose of this research paper is to present a complete review of the most recent achievements in the field of renewable energy forecasting, with a particular emphasis on the use of approaches that involve machine learning and deep learning. Using historical data on weather and energy output, we test the accuracy of a number of different supervised statistical and machine learning techniques, including linear regression, decision tree, and random forest, as well as an artificial neural network-based model called multi-layer perceptron (MLP). This study will address the performance of a variety of machine learning and artificial neural network (ANN) based models, as well as their respective strengths and limitations, and the major elements that determine the reliability of their predictions.