Solar irradiance forecasting can reduce the uncertainty of solar power plant output caused by solar irradiance intermittency. Concentrated solar thermal (CST) plants generate electricity from the direct normal irradiance (DNI) component of solar irradiance. Different forecasting methods have been recommended for a range of forecast horizons relevant to electricity generation. High DNI forecast accuracy is important for achieving accurate forecasts of CST plant output which are shown to increase CST plant profitability. This paper reviews the DNI forecast accuracy of numerical weather prediction models, time series analysis methods, cloud motion vectors, and hybrid methods. The results of the reviewed papers are summarised to identify the best DNI forecast accuracy for particular forecast horizons. The application of DNI forecasts to operate CST plants is also briefly reviewed. This paper found that additional research is required for time series analysis methods to corroborate current results and for satellite-based cloud motion vectors to establish DNI forecast accuracy. It was also concluded that future research should use the same error metrics to report results to facilitate fair comparison of DNI forecast accuracy from different studies. In addition, the creation of a common high quality DNI data set to evaluate all forecasting methods would also help to verify best forecast accuracy. The review of DNI forecasting for CST plants found that using accurate 2-day ahead DNI forecasts can increase revenue and decrease penalty costs. Future research should investigate benefits from using short-term DNI forecasts from the intra-hour forecast horizon up to the 6-h forecast horizon to determine CST plant operation. Another aspect to research is to determine whether the benefit of DNI forecasts for a CST plant is affected by different regulations in different electricity markets. (C) 2014 Elsevier Ltd. All rights reserved.