Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization

doi:10.1016/j.jhydrol.2019.06.045

CORC > 成都山地灾害与环境研究所 > 中国科学院水利部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

	Operation rule derivation of hydropower reservoir by k-means clustering method and extreme learning machine based on particle swarm optimization
	Feng, Zhong-kai 1; Niu, Wen-jing 2; Zhang, Rui3 ; Wang, Sen 4; Cheng, Chun-tian 5
刊名	JOURNAL OF HYDROLOGY
	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.
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关键词	PEAK SHAVING OPERATION ; SYSTEM OPERATION ; WATER ; MODEL ; PSO ; PERFORMANCE ; SIMULATION ; GENERATION ; PREDICTION ; MANAGEMENT
WOS研究方向	Engineering ; Geology ; Water Resources
语种	英语
出版者	ELSEVIER
WOS记录号	WOS:000486092200018
资助机构	National Natural Science Foundation of China ; Natural Science Foundation of Hubei Province ; Fundamental Research Funds for the Central Universities
内容类型	期刊论文
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.