Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree
文献类型：期刊论文
| 作者 | Wang, Lunche; Kisi, Ozgur; Zounemat-Kermani, Mohammad; Zhu, Zhongmin; Gong, Wei; Niu, Zigeng; Liu, Hongfu; Liu, Zhengjia |
| 出版日期 | 2016 |
| 卷号 | 0期号:0 |
| 英文摘要 | Solar radiation is one of the major factors for agricultural, meteorological and ecological applications. In this study, two different optimized adaptive neuro-fuzzy inference systems (ANFIS), ANFIS with grid partition (ANFIS-GP) and ANFIS with subtractive clustering (ANFIS-SC), and M5Tree (M5Tree) methods are proposed for modelling daily global solar radiation (G). Daily meteorological variables at 21 stations in China are used for training and testing the applied models, which is evaluated through root mean square errors (RMSE), mean absolute errors (MAE) and determination coefficient (R2). Above models will be compared with a calibrated empirical Ångström model and the results indicate that the ANFIS models provide better accuracy than the M5Tree and empirical method, for example, the RMSE values for ANFIS-SC, ANFIS-GP, M5Tree and the Ångström model range 2.10-3.08, 2.07-3.08, 2.79-3.87 and 2.54-3.69MJm-2day-1, respectively. The model performances also show some differences at different stations for each model, for example, the ANFIS models produce the most accurate estimations at station 58238, while M5Tree brings the best accuracy at the station 51777. Meanwhile, the models underestimate high radiation values for some stations, which may due to the differences in training and testing data ranges and distribution of the stations. Finally, the reasons for the differences in model performance are investigated in detail. © 2016 Royal Meteorological Society |
| 收录类别 | EI |
| 语种 | 英语 |
| WOS记录号 | WOS:20161902369316 |
| 源URL | [http://ir.radi.ac.cn/handle/183411/39694]  |
| 专题 | 遥感与数字地球研究所_SCI/EI期刊论文_期刊论文 |
| 推荐引用方式 GB/T 7714 | Wang, Lunche,Kisi, Ozgur,Zounemat-Kermani, Mohammad,et al. Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree[J],2016,0(0). |
| APA | Wang, Lunche.,Kisi, Ozgur.,Zounemat-Kermani, Mohammad.,Zhu, Zhongmin.,Gong, Wei.,...&Liu, Zhengjia.(2016).Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree.,0(0). |
| MLA | Wang, Lunche,et al."Prediction of solar radiation in China using different adaptive neuro-fuzzy methods and M5 model tree".0.0(2016). |
入库方式： OAI收割
来源：遥感与数字地球研究所
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