水库群运行的改进深度神经网络模拟方法

流域大规模水库群的形成导致径流时空分布发生深刻变化，而不同水库群往往分属不同业主调度管理，上游水库群的下泄计划无法实时获取，给下游水库调度计划编制带来困难，并且影响下游水库运行安全。本文提出一种水库群运行自适应矩估计改进深度神经网络模拟方法，通过改善深度神经网络参数训练方式，从水库群历史运行数据中提取调度规则，在此基础上对水库群运行进行模拟，并结合实例研究，将结果与传统神经网络方法进行综合对比。结果表明，本文所提出的方法能够更好地模拟水库群运行，所模拟的观音岩、锦屏一级和二滩水库下泄流量平均相对误差分别为8%、11%和10%，均优于反向传播（BP）神经网络结果，可为探究调度计划未知情况下的水库运行规律提供新途径。

Improved deep neural network simulation method for multi-reservoir operation

Construction of reservoirs across river basins has profoundly changed the temporal and spatial distribution of river runoff. The operator of a downstream reservoir cannot obtain in real time the discharge plan of the upstream ones, as reservoirs are often operated by different owners. This not only makes it difficult to prepare an operation plan for the downstream reservoir, but also imposes a significant impact on its safety. This paper describes an adaptive-moment-estimation-improved deep neural network (Adam-DNN) simulation method for extracting operation rules from the historical operation data of different reservoirs and simulating their operation process by these rules. We verify this new method through comparison of its calculation results with those of the back-propagation (BP) neural network. Case studies reveal its average relative errors of 8%, 11% and 10% in discharge simulations for the reservoirs of Guanyinyan, Jinpingyiji and Ertan, respectively, much lower than the BP’s counterparts. The results show our Adam-DNN method can provide a new way to explore the operation rule of a reservoir with unknown operation plan.