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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 AuthorFeng, Zhong-kai(myfellow@163.com)
2019-09-01
Source PublicationJOURNAL OF HYDROLOGY
ISSN0022-1694
Volume576Pages:229-238
AbstractIn 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.
KeywordHydropower reservoir Operation rule derivation k-Means clustering Extreme learning machine Particle swarm optimization
DOI10.1016/j.jhydrol.2019.06.045
Indexed BySCI
WOS KeywordPEAK SHAVING OPERATION ; SYSTEM OPERATION ; WATER ; MODEL ; PSO ; PERFORMANCE ; SIMULATION ; GENERATION ; PREDICTION ; MANAGEMENT
Language英语
Funding ProjectNational Natural Science Foundation of China[51709119] ; Natural Science Foundation of Hubei Province[2018CFB573] ; Fundamental Research Funds for the Central Universities[HUST: 2017KFYXJJ193]
WOS Research AreaEngineering ; Geology ; Water Resources
WOS SubjectEngineering, Civil ; Geosciences, Multidisciplinary ; Water Resources
WOS IDWOS:000486092200018
Funding OrganizationNational Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Fundamental Research Funds for the Central Universities
PublisherELSEVIER
Citation statistics
Document Type期刊论文
Identifierhttp://ir.imde.ac.cn/handle/131551/26627
Collection山地灾害与地表过程重点实验室
Corresponding AuthorFeng, Zhong-kai
Affiliation1.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.
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