"Comparative Study of Short-Term and Long-Term Solar Power Forecasting Using Satellite Data and Machine Learning"

Abstract

The topic of this review paper is "Machine Learning Approach for Short-Term and Long-Term Solar Power Forecasting Using Satellite Data." It looks at three well-known journal pieces that all talk about the same thing. It compares similar pieces of work, looks for gaps in the study, and gives a full account of what was found and observed. This well-planned study tellsuseful things about the issues and progress in predicting solar power, and it also sets the stage for future research. With an emphasis on dependability and accuracy, this study wants to add to the academic discussion about useful ways to guess how much solar power will be used. Among the main data sources used, this review paper applies machine learning based on satellite data to identify both short- and long-term solar power forecasting. Additionally, it focuses on solar power forecasts because of the quickly rising share of renewable sources. Establishing machine learning algorithms and ensuring that good quality satellite data exists are among the hurdles. To make solar power forecasting accurate and efficient and speed up the transition to clean energy sources, concluding remarks highlight in such a way that a set of standardised evaluation metrics must be implemented, and the exercised has to be done in a team.