%0 Journal Article %1 Sfetsos2000169 %A Sfetsos, A. %A Coonick, A.H. %D 2000 %J Solar Energy %K %N 2 %P 169 - 178 %R http://dx.doi.org/10.1016/S0038-092X(99)00064-X %T Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques %U http://www.sciencedirect.com/science/article/pii/S0038092X9900064X %V 68 %X This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters.

@article{Sfetsos2000169, abstract = {This paper introduces a new approach for the forecasting of mean hourly global solar radiation received by a horizontal surface. In addition to the traditional linear methods, several artificial-intelligence-based techniques are studied. These include linear, feed-forward, recurrent Elman and Radial Basis neural networks alongside the adaptive neuro-fuzzy inference scheme. The problem is examined initially for the univariate case, and is extended to include additional meteorological parameters in the process of estimating the optimum model. The results indicate that the developed artificial intelligence models predict the solar radiation time series more effectively compared to the conventional procedures based on the clearness index. The forecasting ability of some models can be further enhanced with the use of additional meteorological parameters. }, added-at = {2014-02-03T18:15:42.000+0100}, annotation = {Hourly, no strong conclusion}, author = {Sfetsos, A. and Coonick, A.H.}, biburl = {https://www.bibsonomy.org/bibtex/27a78593adb850a22e1825f8f2935c3b6/alhirzel}, doi = {http://dx.doi.org/10.1016/S0038-092X(99)00064-X}, file = {Sfetsos2000169.pdf:Sfetsos2000169.pdf:PDF}, groups = {public}, interhash = {7266f3f713abbf997bc1365f8fcd4d42}, intrahash = {7a78593adb850a22e1825f8f2935c3b6}, issn = {0038-092X}, journal = {Solar Energy }, keywords = {}, number = 2, pages = {169 - 178}, timestamp = {2014-02-03T18:15:42.000+0100}, title = {Univariate and multivariate forecasting of hourly solar radiation with artificial intelligence techniques }, url = {http://www.sciencedirect.com/science/article/pii/S0038092X9900064X}, username = {alhirzel}, volume = 68, year = 2000 }