| Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization | |
Feng, Zhong-kai1; Niu, Wen-jing2; Zhang, Rui3 ; Wang, Sen4; Cheng, Chun-tian5
| |
| Corresponding Author | Feng, Zhong-kai(myfellow@163.com) |
| 2019-09-01 | |
| Source Publication | JOURNAL OF HYDROLOGY
 |
| ISSN | 0022-1694 |
| Volume | 576Pages:229-238 |
| Abstract | 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. |
| Keyword | Hydropower reservoir Operation rule derivation k-Means clustering Extreme learning machine Particle swarm optimization |
| DOI | 10.1016/j.jhydrol.2019.06.045 |
| Indexed By | SCI |
| WOS Keyword | PEAK SHAVING OPERATION ; SYSTEM OPERATION ; WATER ; MODEL ; PSO ; PERFORMANCE ; SIMULATION ; GENERATION ; PREDICTION ; MANAGEMENT |
| Language | 英语 |
| Funding Project | National Natural Science Foundation of China[51709119] ; Natural Science Foundation of Hubei Province[2018CFB573] ; Fundamental Research Funds for the Central Universities[HUST: 2017KFYXJJ193] |
| WOS Research Area | Engineering ; Geology ; Water Resources |
| WOS Subject | Engineering, Civil ; Geosciences, Multidisciplinary ; Water Resources |
| WOS ID | WOS:000486092200018 |
| Funding Organization | National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Fundamental Research Funds for the Central Universities |
| Publisher | ELSEVIER |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/26627 |
| Collection | 山地灾害与地表过程重点实验室 |
| Corresponding Author | Feng, Zhong-kai |
| Affiliation | 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 |
| Recommended Citation 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. |
| Files in This Item: | ||||||
| File Name/Size | DocType | Version | Access | License | ||
| 10.1016@j.jhydrol.20（757KB） | 期刊论文 | 出版稿 | 开放获取 | CC BY-NC-SA | View Application Full Text | |
Items in the repository are protected by copyright, with all rights reserved, unless otherwise indicated.
Edit Comment