A novel hybrid model based on artificial neural networks for solar radiation prediction


We propose the SOM-OPELM hybrid model for short term solar radiation prediction.

The proposed model with DirRec strategy or MISMO strategy outperforms the one with Recursive strategy.

The hybrid model outperforms than the BP model and ARIMA model.


As a kind of clean, substantial and renewable energy, solar energy can reduce environmental pollution with an extensive application potential. Precise prediction of global solar radiation has great significance for the design of solar energy systems and management of solar power plants.

In this paper, a new hybrid model combining the SOM-OPELM with time series strategies is presented for predicting the global solar radiation on the horizon. In this model, the SOM divides the original data into distinct clusters and the OPELM establishes the prediction model. Subsequently, three population time series strategies, (i.e. Recursive strategy, DirRec strategy and MISMO strategy) are adopted to accomplish the multi-step prediction. A comparison between the proposed SOM-OPELM model and other conventional methods is carried out to demonstrate its efficiency and estimation performance. The simulation results show that the proposed SOM-OPELM model with DirRec strategy or MISMO strategy outperforms the following models: Recursive-BP, DirRec-BP, MISMO-BP, Recursive-SOM-OPELM and ARIMA.


  • Time series strategy;
  • Multi-step-ahead predict;
  • OPELM;
  • Energy prediction

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