Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction
文献类型：期刊论文
| 作者 | Duan, Guosheng1; Wu, Lifeng2,3; Liu, Fa4; Wang, Yicheng2; Wu, Shaofei3 |
| 刊名 | SUSTAINABILITY
 |
| 出版日期 | 2022-06-01 |
| 卷号 | 14期号:11页码:20 |
| 关键词 | forecasting solar radiation Global Ensemble Forecast System bat algorithm |
| DOI | 10.3390/su14116824 |
| 通讯作者 | Wu, Lifeng(lifengwu@nit.edu.cn) |
| 英文摘要 | Accurate forecasting of solar radiation (Rs) is significant to photovoltaic power generation and agricultural management. The National Centers for Environmental Prediction (NECP) has released its latest Global Ensemble Forecast System version 12 (GEFSv12) prediction product; however, the capability of this numerical weather product for Rs forecasting has not been evaluated. This study intends to establish a coupling algorithm based on a bat algorithm (BA) and Kernel-based nonlinear extension of Arps decline (KNEA) for post-processing 1-3 d ahead Rs forecasting based on the GEFSv12 in Xinjiang of China. The new model also compares two empirical statistical methods, which were quantile mapping (QM) and Equiratio cumulative distribution function matching (EDCDFm), and compares six machine-learning methods, e.g., long-short term memory (LSTM), support vector machine (SVM), XGBoost, KNEA, BA-SVM, BA-XGBoost. The results show that the accuracy of forecasting Rs from all of the models decreases with the extension of the forecast period. Compared with the GEFS raw Rs data over the four stations, the RMSE and MAE of QM and EDCDFm models decreased by 20% and 15%, respectively. In addition, the BA-KNEA model was superior to the GEFSv12 raw Rs data and other post-processing methods, with R-2 = 0.782-0.829, RMSE = 3.240-3.685 MJ m(-2) d(-1), MAE = 2.465-2.799 MJ m(-2) d(-1), and NRMSE = 0.152-0.173. |
| WOS关键词 | SUPPORT VECTOR MACHINE ; GLOBAL HORIZONTAL IRRADIANCE ; ARPS DECLINE MODEL ; SUNSHINE DURATION ; AIR-TEMPERATURE ; ANFIS ; POLLUTION ; SVM |
| 资助项目 | National Natural Science Foundation of China[51879226] ; National Natural Science Foundation of China[51709143] ; Jiangxi Natural Science Foundation of China[20181BBG78078] |
| WOS研究方向 | Science & Technology - Other Topics ; Environmental Sciences & Ecology |
| 语种 | 英语 |
| WOS记录号 | WOS:000809897600001 |
| 出版者 | MDPI |
| 资助机构 | National Natural Science Foundation of China ; Jiangxi Natural Science Foundation of China |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/178846]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Wu, Lifeng |
| 作者单位 | 1.Beijing Normal Univ, Fac Geog Sci, Beijing 100875, Peoples R China 2.China Inst Water Resources & Hydropower Res, State Key Lab Simulat & Regulat Water Cycle River, Beijing 100038, Peoples R China 3.Nanchang Inst Technol, Sch Hydraul & Ecol Engn, Nanchang 330099, Jiangxi, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Sci & Nat Resources Res, Key Lab Water Cycle & Related Land Surface Proc, Beijing 100101, Peoples R China |
| 推荐引用方式 GB/T 7714 | Duan, Guosheng,Wu, Lifeng,Liu, Fa,et al. Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction[J]. SUSTAINABILITY,2022,14(11):20. |
| APA | Duan, Guosheng,Wu, Lifeng,Liu, Fa,Wang, Yicheng,&Wu, Shaofei.(2022).Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction.SUSTAINABILITY,14(11),20. |
| MLA | Duan, Guosheng,et al."Improvement in Solar-Radiation Forecasting Based on Evolutionary KNEA Method and Numerical Weather Prediction".SUSTAINABILITY 14.11(2022):20. |
入库方式： OAI收割
来源：地理科学与资源研究所
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