KMS Of Academy of mathematics and systems sciences, CAS
| Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm | |
| Hu, Jianming1; Heng, Jiani2; Wen, Jiemei1; Zhao, Weigang3 | |
| 2020-12-01 | |
| Source Publication | RENEWABLE ENERGY
 |
| ISSN | 0960-1481 |
| Volume | 162Pages:1208-1226 |
| Abstract | Wind energy has become a kind of attractive alternative energy in power generation field due to its nonpolluting and renewable properties. Wind speed forecasting acts an important role in programming and operation of power systems. However, achieving high precision wind speed forecasts is still consider as an arduous and challenging issue with the randomization and transient exist in wind speed time series. For this reason, this paper proposed two novel de-noising-reconstruction-based hybrid models which consist of novel signal decomposed methods, feature selection approaches and predictors based on quantile regression and optimization algorithm to achieve more accurate short term wind speed forecasting. The developed hybrid models firstly eliminate inherent noise from the wind speed sequences via decomposed method and subsequently construct the appropriate datasets for the forecasting engines by adopting the feature selection method; finally, establish the predictors for the forecasting task. To verify the effectiveness of proposed forecasting models, 1-h and 2-h wind speed data collected from Yumen, Gansu province of China mainland is used as case studies. The computational results demonstrated that the developed hybrid models yield better performance contrast with those of other models involved in this research in terms of both wind speed deterministic and probabilistic forecasting. (c) 2020 Elsevier Ltd. All rights reserved. |
| Keyword | Renewable energy Complete empirical mode decomposition with adaptive noise Quantile regression neural network Wind speed forecasting Distance correlation |
| DOI | 10.1016/j.renene.2020.08.077 |
| Indexed By | SCI |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[71701053] ; National Natural Science Foundation of China[71601020] ; Natural Science Foundation of Guangdong Province[Grant2020A151501527] ; Guangzhou University Research Fund[220030401] ; Guangzhou University Research Fund[6962091190] |
| WOS Research Area | Science & Technology - Other Topics ; Energy & Fuels |
| WOS Subject | Green & Sustainable Science & Technology ; Energy & Fuels |
| WOS ID | WOS:000590672700009 |
| Publisher | PERGAMON-ELSEVIER SCIENCE LTD |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.amss.ac.cn/handle/2S8OKBNM/57799 |
| Collection | 中国科学院数学与系统科学研究院 |
| Corresponding Author | Heng, Jiani |
| Affiliation | 1.Guangzhou Univ, Coll Econ & Stat, Guangzhou, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China 3.Beijing Inst Technol, Sch Management & Econ, Beijing, Peoples R China |
| Recommended Citation GB/T 7714 | Hu, Jianming,Heng, Jiani,Wen, Jiemei,et al. Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm[J]. RENEWABLE ENERGY,2020,162:1208-1226. |
| APA | Hu, Jianming,Heng, Jiani,Wen, Jiemei,&Zhao, Weigang.(2020).Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm.RENEWABLE ENERGY,162,1208-1226. |
| MLA | Hu, Jianming,et al."Deterministic and probabilistic wind speed forecasting with de-noising-reconstruction strategy and quantile regression based algorithm".RENEWABLE ENERGY 162(2020):1208-1226. |
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