耦合深度学习的水丰水库入库径流中长期预测方法研究  

Study on Medium and Long Term Forecasting Methods for Inflow Runoff of Shuifeng Reservoir Coupled with Deep Learning

作　　者：崔杰连 常亮 赵敏 孟宪明 孙皓晨 董前进[2] CUI Jie-lian;CHANG Liang;ZHAO Min;MENG Xian-ming;SUN Hao-chen;DONG Qian-jin(Taipingwan power plant of Lvyuan hydropower company,northeast branch of State grid,Dandong 116000,Liaoning Province,China;State Key Laboratory of Water Resources Engineering and Management,Wuhan University,Wuhan 430072,Hubei Province,China)

机构地区：[1]国网东北分部绿源水力发电公司太平湾发电厂,辽宁丹东116000 [2]武汉大学水资源工程与调度全国重点实验室,湖北武汉430072

出　　处：《中国农村水利水电》2024年第8期73-80,共8页China Rural Water and Hydropower

基　　金：国家自然科学基金项目(52279024,51979198);国家电网有限公司科技项目(SGDBLYTPJWJS2310094)。

摘　　要：少资料地区径流中长期预测关系发电厂中长期发电量的多寡,也对电厂短期经济运行有较强的指导作用。鸭绿江流域是东北地区重要的清洁能源基地,由于朝鲜控制了鸭绿江流域超过一半的面积,但其径流数据难以与中方共享,给鸭绿江流域径流中长期预测带来一定的阻碍。以鸭绿江流域水丰水库入库径流为研究对象,分别采用相空间重构模型(局域法、全局法)、LSTM模型、小波分析-LSTM模型、耦合相空间重构(局域法、全局法)和小波分析模型共6个模型方法对水丰水库旬、月及年尺度入库径流进行中长期径流预报工作,以平均绝对误差、平均绝对百分比误差与合格率对上述6个模型的预测结果进行精度评比。结果表明,年径流预报采用耦合相空间重构(全局法)和小波分析模型;月尺度径流预报中,1月预见期1-5月采用耦合相空间重构(局域法)和小波分析模型以及小波分析-LSTM模型效果较好,而6-12月耦合相空间重构(全局法)和小波分析模型具有明显优势;1年预见期中,小波分析-LSTM模型效果较好。旬尺度径流预测,1旬预见期采用小波分析-LSTM模型效果较好,3旬预见期采用小波分析-LSTM模型或耦合相空间重构(全局法)和小波分析模型,1年预见期中耦合相空间重构(全局法)和小波分析模型有明显优势。研究将为水丰水库及下游发电厂制定中长期调度计划提供支持。The medium and long term forecast of runoff in areas with little data is related to the medium and long term power generation of power plants,and also has a strong guiding effect on the short term economic operation of power plants.The Yalu River basin is an important clean energy base in Northeast China.Since North Korea controls more than half of the area of the Yalu River basin,it is difficult to share its runoff data with China,which brings certain obstacles to the medium-and long-term runoff forecast of the Yalu River basin.Taking the inflow runoff of Shuifeng Reservoir in Yalu River Basin as the research object,six model methods,namely phase space reconstruction model(local method,global method),LSTM model,wavelet analysis-LSTM model,coupled phase space reconstruction(local method,global method)and wavelet analysis model,were used to forecast the inflow runoff of Shuifeng reservoir in the medium-and long-term.The accuracy of the prediction results of the above six models was compared by mean absolute error,mean absolute percentage error and qualification rate.The results show that the coupled phase space reconstruction(global method)and wavelet analysis model are used to forecast the annual runoff.In the monthly scale runoff forecast,the results of coupled phase space reconstruction(local method),wavelet analysis model and wavelet analysis-LSTM model are better from January to May,while the coupled phase space reconstruction(global method)and wavelet analysis model have obvious advantages from June to December.In the 1-year forecast period,the wavelet analysis-LSTM model has a good effect.For the ten-day runoff prediction,the effect of wavelet analysis-LSTM model is better in the 1-day forecast period,and that of coupled phase space reconstruction(global method)and wavelet analysis model in the 3-day forecast period has obvious advantages.The study will support the formulation of medium-and long-term operation plans for Shuifeng Reservoir and downstream power plants.

关 键 词：中长期径流预报 相空间重构 小波分析 小波分析-LSTM模型 鸭绿江流域 

分 类 号：P338.2[天文地球—水文科学]