Optimization of Solar Energy Systems Using Machine Learning Techniques

Abstract

The increasing global demand for clean and sustainable energy has accelerated the adoption of solar energy systems. However, the efficiency of solar power generation is influenced by various dynamic factors such as weather conditions, panel orientation, dust accumulation, and system degradation. Traditional optimization techniques often fail to capture the nonlinear and complex relationships among these variables. This study explores the application of Machine Learning (ML) techniques to optimize solar energy systems, focusing on performance prediction, fault detection, and energy yield maximization. Various ML algorithms including Artificial Neural Networks (ANN), Support Vector Machines (SVM), Random Forest (RF), and Deep Learning models are evaluated. The results demonstrate that ML-driven optimization significantly enhances system efficiency, reduces operational costs, and improves reliability. The paper also discusses implementation challenges and future research directions.