The volatility and intermittency of solar energy seriously restrict the development of the photovoltaic (PV) industry. Accurate forecast of short-term PV power generation is essential for the optimal balance and dispatch of power plants in the smart grid. This article presents a machine learning approach for analyzing the volt-ampere characteristics and influential factors on PV data. A correlation analysis is employed to discover some hidden characteristic variables. Then, an adaptive ensemble method with stochastic configuration networks as base models (AE-SCN) is proposed to construct the PV prediction model, which integrates bagging and adaptive weighted data fusion algorithms. Compared with the original SCN, SCN ensemble (SCNE) and random vector functional-link network (RVFLN), linear regression model, random forest model and autoregressive integrated moving average (ARMA) model, AE-SCN performs favorably in the terms of the prediction accuracy.