An aggregation-decomposition bayesian stochastic optimization model for cascade hydropower reservoirs using medium-range precipitation forecasts

被引：1
作者：
Peng, Yong [1 ]
Xu, Wei [2 ]
Zhang, Xiaoli [3 ]
机构：
[1] Dalian Univ Technol, Sch Hydraul Engn, Dalian, Peoples R China
[2] Chongqing Jiaotong Univ, Coll River & Ocean Engn, Chongqing, Peoples R China
[3] North China Univ Water Resources & Elect, Sch Water Conservancy, Zhengzhou, Henan, Peoples R China
来源：
2ND ANNUAL INTERNATIONAL CONFERENCE ON INFORMATION SYSTEM AND ARTIFICIAL INTELLIGENCE (ISAI2017) | 2017年 / 887卷
关键词：
D O I：
10.1088/1742-6596/887/1/012005
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
The forecast information is essential to improve the utilization efficiency of hydropower resources. To address the uncertainties of forecasting inflow, the Aggregation-Decomposition Bayesian Stochastic Dynamic Programming (AD-BSDP) model is presented in the present paper by using the 10-days precipitation value of the Quantitative Precipitation Forecasts from Global Forecast System (GFS-QPFs). The application in China's Hun River cascade hydropower reservoirs shows that the GFS-QPFs are beneficial for hydropower generation and the performance of AD-BSDP is more efficiency and reliability than the others models.
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页数：8
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