热门
Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization
文献类型：期刊论文
| 作者 | Feng, Zhong-kai1; Niu, Wen-jing2; Zhang, Rui3 ; Wang, Sen4; Cheng, Chun-tian5
|
| 刊名 | JOURNAL OF HYDROLOGY
 |
| 出版日期 | 2019-09-01 |
| 卷号 | 576页码:229-238 |
| 关键词 | Hydropower reservoir Operation rule derivation k-Means clustering Extreme learning machine Particle swarm optimization |
| ISSN号 | 0022-1694 |
| DOI | 10.1016/j.jhydrol.2019.06.045 |
| 通讯作者 | Feng, Zhong-kai(myfellow@163.com) |
| 英文摘要 | In practice, the rational operation rule derived from historical information and real-time working condition can help the operators make the quasi-optimal scheduling plan of hydropower reservoirs, leading to significant improvements in the generation benefit. As an emerging artificial intelligence method, the extreme learning machine (ELM) provides a new effective tool to derivate the reservoir operation rule. However, it is difficult for the standard ELM method to avoid falling into local optima due to the random determination of both input-hidden weights and hidden bias. To enhance the ELM performance, this research develops a novel class-based evolutionary extreme learning machine (CEELM) to determine the appropriate operation rule of hydropower reservoir. In CEELM, the k-means clustering method is firstly adopted to divide all the influential factors into several disjointed sub-regions with simpler patterns; and then ELM optimized by particle swarm intelligence is applied to identify the complex input-output relationship in each cluster. The results from two reservoirs of China show that our method can obtain satisfying performance in deriving operation rules of hydropower reservoir. Thus, it can be concluded that the model's generalization capability can be improved by isolating each subclass composed of similar dataset. |
| WOS关键词 | PEAK SHAVING OPERATION ; SYSTEM OPERATION ; WATER ; MODEL ; PSO ; PERFORMANCE ; SIMULATION ; GENERATION ; PREDICTION ; MANAGEMENT |
| 资助项目 | National Natural Science Foundation of China[51709119] ; Natural Science Foundation of Hubei Province[2018CFB573] ; Fundamental Research Funds for the Central Universities[HUST: 2017KFYXJJ193] |
| WOS研究方向 | Engineering ; Geology ; Water Resources |
| 语种 | 英语 |
| WOS记录号 | WOS:000486092200018 |
| 出版者 | ELSEVIER |
| 资助机构 | National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Fundamental Research Funds for the Central Universities |
| 源URL | [http://ir.imde.ac.cn/handle/131551/26627]  |
| 专题 | 成都山地灾害与环境研究所_山地灾害与地表过程重点实验室 |
| 通讯作者 | Feng, Zhong-kai |
| 作者单位 | 1.Huazhong Univ Sci & Technol, Sch Hydropower & Informat Engn, Wuhan 430074, Hubei, Peoples R China 2.ChangJiang Water Resources Commiss, Bur Hydrol, Wuhan 430010, Hubei, Peoples R China 3.Chinese Acad Sci, Inst Mt Hazards & Environm, Chengdu 610041, Sichuan, Peoples R China 4.Minist Water Resources, Key Lab Pearl River Estuarine Dynam & Associated, Guangzhou 510611, Guangdong, Peoples R China 5.Dalian Univ Technol, Inst Hydropower & Hydroinformat, Dalian 116024, Peoples R China |
| 推荐引用方式 GB/T 7714 | Feng, Zhong-kai,Niu, Wen-jing,Zhang, Rui,et al. Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization[J]. JOURNAL OF HYDROLOGY,2019,576:229-238. |
| APA | Feng, Zhong-kai,Niu, Wen-jing,Zhang, Rui,Wang, Sen,&Cheng, Chun-tian.(2019).Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization.JOURNAL OF HYDROLOGY,576,229-238. |
| MLA | Feng, Zhong-kai,et al."Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization".JOURNAL OF HYDROLOGY 576(2019):229-238. |
入库方式： OAI收割
来源：成都山地灾害与环境研究所
浏览0
下载0
收藏0
其他版本
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。