The foundation for many solar energies uses as well as economic and environmental concerns is global solar irradiation information. However, due to solar irradiation and measurements variations, reliable worldwide statistics on solar irradiation are frequently impossible or difficult to acquire. In addition, more precise forecast of solar irradiation plays an increasingly important role in electric energy planning and management due to integrating photovoltaic solar systems into power networks. Hence, this paper proposes a new hybrid model for 1-hour ahead solar irradiation forecasting called LGC- GMDH (local gravitational clustering- Group method of data handling). The novel LGC- GMDH model is based on the local clustering that adequately captures the underlying features of the solar irradiation time series. Each cluster is then forecasted using the GMDH method, which is a self-organized system that is capable of handling very complicated nonlinear problems. Finally, these local forecasts are reconstructed in order to obtain the global forecast. Comparative study between the proposed model and the traditional individual models such as; backpropagation neural network (BP), supporting vector machines (SVM), long short-term memory (LTSM), hybrid models such; BP-MLP, RNN-MLP, LSTM-MLP hybrid wavelet packet decomposition (WPD), convolutional neural network (CNN) with LSTM-MLP, and ANFIS clustering shows that the proposed model overcomes conventional model deficiencies and achieves more precise predicting outcome.