A Novel 2-D Model Approach for the Prediction of Hourly Solar Radiation

In this work, a two-dimensional (2-D) representation of the hourly solar radiation data is proposed. The model enables accurate forecasting using image prediction methods. One year solar radiation data that is acquired and collected between August 1, 2005 and July 30, 2006 in Iki Eylul campus of Anadolu University, and a 2-D representation is formed to construct an image data. The data is in raster scan form, so the rows and columns of the image matrix indicate days and hours, respectively. To test the forecasting efficiency of the model, first 1-D and 2-D optimal 3-tap linear filters are calculated and applied. Then, the forecasting is tested through three input one output feed−forward neural networks (NN). One year data is used for training, and 2 month(from August 1,2006 to September 30,2006) for testing. Optimal linear filters and NN models are compared in the sense of root mean square error (RMSE). It is observed that the 2-D model has advantages over the 1D representation. Furthermore, the NN model accurately converges to forecasting errors smaller than the linear prediction filter results.