 Solar PV power generation is subject to randomness, volatility, and intermittency due to its dependence on weather conditions. Predictive algorithms have been developed to improve the accuracy of short-term solar PV power forecasts, which can be utilized in the daily planning and operation of a smart grid system. Additionally, the predictive methods identified in the reviewed literature are classified according to the input data source, and the case studies and examples proposed are analyzed in detail. Future studies on short-term solar PV power forecasting are also proposed. This article was authored by Wen Chang-sai, Qi Yixing Tu, Qi Minghong, and others.