Hourly solar radiation forecasting using optimal coefficient 2-D linear filters and feed-forward neural networks


SOLAR ENERGY, vol.82, no.8, pp.714-726, 2008 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 82 Issue: 8
  • Publication Date: 2008
  • Doi Number: 10.1016/j.solener.2008.02.003
  • Journal Name: SOLAR ENERGY
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Page Numbers: pp.714-726
  • Keywords: solar radiation, forecasting, linear filter, NN, MODEL, ALGORITHM
  • Anadolu University Affiliated: Yes


In this work, the hourly solar radiation data collected during the period August 1, 2005-July 30, 2006 from the solar observation station in Iki Eylul campus area of Eskisehir region are studied. A two-dimensional (2-D) representation model of the hourly solar radiation data is proposed. The model provides a unique and compact visualization of the data for inspection, and enables accurate forecasting using image processing methods. Using the hourly solar radiation data mentioned above, the image model is formed in raster scan form with rows and columns corresponding to days and hours, respectively. Logically, the between-day correlations along the same hour segment provide the vertical correlations of the image, which is not available in the regular I-D representation. To test the forecasting efficiency of the model, nine different linear filters with various filter-tap configurations are optimized and tested. The results provide the necessary correlation model and prediction directions for obtaining the optimum prediction template for forecasting. Next, the 2-D forecasting performance is tested through feed-forward neural networks (NN) using the same data. The 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 pronounced advantages over the I-D representation for both linear and NN prediction methods. Due to the capability of depicting the nonlinear behavior of the input data, the NN models are found to achieve better forecasting results than linear prediction filters in both 1-D and 2-D. (C) 2008 Elsevier Ltd. All rights reserved.