Effects of temperature and pressure information in a hybrid (fourier series / neural networks) solar radiation model

FİDAN M., Hocaoǧlu F. O., GEREK Ö. N.

2009 4th International Conference on Innovative Computing, Information and Control, ICICIC 2009, Kao-hsiung, Taiwan, 7 - 09 December 2009, pp.667-670 identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icicic.2009.189
  • City: Kao-hsiung
  • Country: Taiwan
  • Page Numbers: pp.667-670
  • Anadolu University Affiliated: Yes


Solar radiation modeling is a critical step in efficient management of solar energy. In this study, a novel solar radiation modeling procedure is developed with the a-priori information of temperature and pressure values, which are naturally dependent on solar radiation via indirect atmospheric phenomena. Firstly, daily behavior of hourly solar radiations is considered in frequency domain. Initial nine Fourier series coefficients are calculated for each day. Secondly, various neural networks models are built for prediction of these nine Fourier coefficients using the input data gathered from early morning hours and previous day. Apart from the solar radiation readings, temperature and pressure data are also used for developing a more accurate model. It is concluded that, the support of temperature and pressure data of the region improves the solar radiation model. Finally, differences between the performances of the proposed models reveal correlative relationships between atmospheric parameters and solar radiation. © 2009 IEEE.