Ensemble Learning Algorithms for Solar Power Prediction in Saudi Arabia: A Data-Driven Approach

Abstract

This paper explores into Saudi Arabia's global leadership in renewable energy, particularly its solar initiatives. The study employs a detailed analysis of input variables, including time, temperature, wind speed, humidity, and air pressure, forming the basis for a predictive model focused on Umax (voltage). Rigorous data analysis establishes the reliability of findings, paving the way for further exploration into the models' inner workings. The paper concludes by highlighting the significance of the research for stakeholders, offering nuanced insights into Umax variations and optimizing solar power generation on a global scale.