Predictive modeling for solar footprint prediction using machine learning

Accurate estimates of solar footprints, especially the potential energy output based on geographical, climatic, and temporal characteristics, are becoming increasingly important as solar energy systems are widely adopted. Solar footprint describes the area of a building, rooftop, or piece of land that can best accommodate solar panels and produce electricity. Machine learning (ML)-based predictive modeling is a viable way to improve solar footprint prediction, facilitating improved solar power generation planning, management, and optimization. This chapter explores the multiple ML methods for predicting solar footprints, the variables that affect them, and the uses and difficulties with accuracy. The simulation results are interpreted as the K-nearest neighbor (KNN) model is efficient in predicting with maximum accuracy in lesser training time.