Researchers are exploring various machine learning models and algorithms to improve photovoltaic power forecasting, including deep learning and hybrid approaches. These methods aim to enhance the accuracy of solar power predictions in different conditions, such as seasonal changes and stress on the energy system. The development of these forecasting models is being studied in the context of smart grids, energy consumption systems, and electrified grids.