Solar power forecasting is crucial for the purpose of ensuring grid stability and proper grid management. Recent advancements with inside the discipline of solar power forecasting are presented, and the main focus is on the different types of Machine Learning (ML) Techniques used. These ML techniques can solve both the complex and nonlinear data structures. The two types of solar power forecasting are direct and indirect. It entails three models namely: plane of array irradiance, estimating solar irradiance forecast, solar performance. For the purpose of classification of solar power forecasting we take into consideration 3 main parameters such as the Forecast Horizon, Input Parameters and the Forecasting methodology. During the failure of the real-time data acquisition or with inside the case of unavailability of solar power for a new PV plant the concept of Indirect solar power forecasting can be used. According to recent studies models like the hybrid models, deep neural networks take over the conventional methods of the short-term solar forecasting. Data-preparation techniques and various intelligent optimizations enhance the performance accuracy.
