基于深度学习的三峡水库实时防洪调度模型北大核心CSCD

水库实时调度需要考虑多种约束条件及综合目标,具有较高复杂度。本文以三峡水库实时防洪调度为研究对象,提出一种基于深度学习的水库实时防洪调度模型。研究模拟三峡水库实时调度过程,生成训练样本数据。基于样本数据生成高维张量输入数据,通过网络参数训练提取高维数据特征以学习拟合水库实时调度模式。基于深度卷积神经网络实时调度模型在训练过程中提取闸门数据特征,模型中采用强化学习算法,迭代优化模型参数,随着样本数据不断更新,通过在线学习实现最优调度决策。实例研究表明,水位实时控制和下泄流量实时控制模型模拟的下泄流量与实际数据相对误差分别为1.4%和1.0%左右,该深度学习模型有较好的收敛性,能够应用于水库实时调度。

Real-time flood control operation model of Three Gorges Reservoir based on deep learning

Real-time flood control scheduling needs to consider multiple constraints and comprehensive objectives, which has a high degree of complexity. Using the Three Gorges reservoir as a study case, this paper describes a new real-time reservoir flood control dispatching model based on deep learning. We simulate the dispatching process of the reservoir to generate training sample data; then using the sample data, we generate high-dimensional tensor input data, and extract high-dimensional data features through network parameter training to learn how to fit real-time reservoir flood control dispatch modes. This dispatching model, based on a real-time scheduling model equipped with a deep convolutional neural network, extracts the characteristic information of the gate opening in the training process. It uses reinforcement learning algorithms for optimizing the model parameters iteratively, and updating sample data successively through online learning to implement optimal scheduling decisions. In the case study of the reservoir’s real-time flood level control model and real-time discharge flow control model, the relative errors of simulated discharges are around 1.4% and 1.0%, respectively, against the site observed data. And the calculations show that our deep learning model has a good convergence, and it is applicable to the real-time flood control dispatching of reservoirs.