Photovoltaic (PV) power intermittence impacts electrical grid security and operation. Precise PV power and solar irradiation forecasts have been investigated as significant reducers of such impacts. Predicting solar irradiation involves uncertainties related to the characteristics of time series and their high volatility due to the dependence on many weather conditions. We propose a systematic review of PV power and solar resource forecasting, considering technical aspects related to each applied methodology. Our review covers the performance analysis of various physical, statistical, and machine learning models. These methodologies should contribute to decision-making, being applicable to different sites and climatic conditions. About 42% of the analyzed articles developed hybrid approaches, 83% performed short-term prediction, and more than 78% had, as forecast goal, PV power, solar irradiance, and solar irradiation. Considering spatial forecast scale, 66% predicted in a single field. As a trend for the coming years, we highlight the use of hybridized methodologies, especially those that optimize input and method parameters without loss of precision and postprocessing methodologies aiming at improvements in individualized applications.