Proceedings of the Korea Water Resources Association Conference (한국수자원학회:학술대회논문집)
- 2015.05a
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- Pages.483-486
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- 2015
adaptive neuro-fuzzy inference system;daily solar radiation;Illinois;limited weather variables;
- Kim, Sungwon (Department of Railroad and Civil Engineering, Dongyang University)
- Published : 2015.05.27
Abstract
The objective of this study is to develop generalized regression neural networks (GRNN) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using GRNN model. From the performance evaluation and scatter diagrams of GRNN model, GRNN 3 (three input) model produces the best results for both stations. Results obtained indicate that GRNN model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois. These results testify the generation capability of GRNN model and its ability to produce accurate estimates in Illinois.
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