Solar Power Forecasting Using Machine Learning Techniques

Abstract

The world faces a major global issue of increasing global warming and energy demands. There is an increasing need for renewable and eco-friendly energy sources that produce little greenhouse emissions. Hydro projects need a massive investment; likewise, wind energy is limited to coastal regions. Solar energy investments offer the same or even more benefits at a considerable cost. Tackling these issues, this chapter presents a comprehensive approach for predicting solar power generation using machine learning techniques. The study uses a dataset of 21 meteorological features, the critical being temperature ranges. Various visualization techniques are employed to understand the nature of variables. Preprocessing methods, such as removing constant and duplicate features, and handling data imbalance using SMOTE are applied. Three machine learning regression models—linear regression, elastic net regression, and random forest regression—are compared to identify the best-performing method. Through extensive testing, the study achieved an R2 score of 0.964.