基于机器学习的黄河上游梯级水库群多目标优化调度规则提取  

Extracting multi-objective optimal operation rules of cascade reservoirs in upper reaches of Yellow River based on machine learning

作　　者：钟加星 董增川[3] 孟金玉 王凯 胡友兵 赵梦杰 ZHONG Jiaxing;DONG Zengchuan;MENG Jinyu;WANG Kai;HU Youbing;ZHAO Mengjie(Hydrological Bureau(Information Center),Huaihe River Water Resources Commission,Bengbu 233001,China;Laboratory of Hydrological Monitoring and Forecasting of Huaihe River Basin,Bengbu 233001,China;College of Hydrology and Water Resources,Hohai University,Nanjing 210098 China)

机构地区：[1]淮河水利委员会水文局(信息中心),安徽蚌埠233001 [2]淮河流域水文测报重点实验室,安徽蚌埠233001 [3]河海大学水文水资源学院,江苏南京210098

出　　处：《河海大学学报（自然科学版）》2024年第6期30-37,共8页Journal of Hohai University(Natural Sciences)

基　　金：国家重点研发计划项目(2016YFC0402209);江苏省水利科技项目(2022042)。

摘　　要：为最大化发挥多功能水库综合效益,构建了黄河上游梯级水库群发电、供水、防洪、防凌、生态多目标优化调度模型,采用多元线性回归模型、随机森林模型和LightGBM模型提取了梯级库群多目标调度规则,并通过改进泰勒图和典型调度方案对提取效果进行了评价。结果表明:随机森林模型和LightGBM模型对调度规则提取的效果整体优于传统的多元线性回归模型,LightGBM模型训练结果更有实际指导意义,在发电、供水及均衡调度需求下均能有效指导水库贴近优化下泄过程。To maximize the comprehensive benefits of multifunctional reservoirs,a multi-objective optimization operation model for power generation,water supply,flood control,ice prevention,and ecology of cascade reservoirs in the upper reaches of the Yellow River was constructed.Multiple linear regression model,random forest model,and LightGBM machine learning model were combined to extract multi-objective operation rules for cascade reservoirs,and the extraction effect was evaluated by combining improved Taylor plots and typical operation schemes.The results show that the two machine learning models have overall better performance in extracting operation rules than traditional multiple linear regression model.The newly introduced LightGBM model training results have more practical guidance significance,and can effectively guide the optimization of reservoir discharge under power generation,water supply,and balanced operation needs.

关 键 词：梯级水库群 多目标调度 调度规则 机器学习 黄河上游 

分 类 号：TV697.1[水利工程—水利水电工程]