We evaluate and compare several forecasting techniques using no exogenous inputs for predicting the solar power output of a 1 MWp, single-axis tracking, photovoltaic power plant operating in Merced, California. The production data used in this work corresponds to hourly averaged power collected from November 2009 to August 2011. Data prior to January 2011 is used to train the several forecasting models for the 1 and 2 h-ahead hourly averaged power output. The methods studied in this work are: Persistent model, Auto-Regressive Integrated Moving Average (ARIMA), k-Nearest-Neighbors (kNNs), Artificial Neural Networks (ANNs), and ANNs optimized by Genetic Algorithms (GAs/ANN). The accuracy of the models is determined by computing error statistics such as mean absolute error (MAE), mean bias error (MBE), and the coefficient of correlation (R-2) for the differences between the forecasted values and the measured values for the period from January to August of 2011. This work also addresses the accuracy of the different methods as a function of the variability of the power output, which depends strongly on seasonal conditions. The findings show that the ANN-based forecasting models perform better than the other forecasting techniques, that substantial improvements can be achieved with a GA optimization of the ANN parameters, and that the accuracy of all models depends strongly on seasonal characteristics of solar variability. (C) 2012 Elsevier Ltd. All rights reserved.