KMS Institute of Mountain Hazards and Environment,CAS
| 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
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| 2019 | |
| Source Publication | Journal of Hydrology
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| ISSN | 0022-1694 |
| Volume | 576Pages:229-238 |
| Subtype | Article |
| Contribution Rank | 3 |
| 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. © 2019 Elsevier B.V. |
| Keyword | Cluster analysis Hydroelectric power Knowledge acquisition Learning systems Particle swarm optimization (PSO) Swarm intelligence |
| DOI | 10.1016/j.jhydrol.2019.06.045 |
| Indexed By | EI |
| Language | 英语 |
| EI Accession Number | Accession number:20192607091938 |
| Citation statistics | |
| Document Type | 期刊论文 |
| Identifier | http://ir.imde.ac.cn/handle/131551/26627 |
| Collection | 中国科学院水利部成都山地灾害与环境研究所 |
| Corresponding Author | Feng Zhong-kai |
| Affiliation | 1.School of Hydropower and Information Engineering, Huazhong University of Science and Technology, Wuhan;430074, China; 2.Bureau of Hydrology, ChangJiang Water Resources Commission, Wuhan;430010, China; 3.Institute of Mountain Hazards and Environment, Chinese Academy of Sciences, Chengdu;610041, China; 4.Key Laboratory of the Pearl River Estuarine Dynamics and Associated Process Regulation, Ministry of Water Resources, Guangzhou;510611, China; 5.Institute of Hydropower and Hydroinformatics, Dalian University of Technology, Dalian;116024, 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 | |
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