Solar radiation forecasting is an important part of planning and sizing of a photovoltaic power plant. Yearly measured hourly solar radiation data on the surface of a region include both stochastic and deterministic behaviors. The deterministic part comes from the solar geometry whereas the stochastic part is occurred due to random atmospheric events such as the motion of clouds etc. Moving from these facts, in this paper two different adaptive approaches are developed and tested for hourly solar radiation forecasting. In first approach, the data is separated into seasons. For winter and summer season it is thought that linear predictors work better due to rare alterations for short time periods. For these seasons linear prediction approach is adopted and used. On the other hand bigger alterations are most probable for spring and fall seasons. Therefore, for these seasons an empirical method is employed. In second approach, clearness index is considered as a decision maker to decide whether linear or empirical method will be used as a predictor. This decision is adopted for each prediction. It is obtained from the results that such an adoptive method outperforms non adoptive ones. (C) 2015 Elsevier Ltd. All rights reserved.