物理机制引导的水库调度深度学习模型研究

Deep learning model guided by physical mechanism for reservoir operation

为了从海量数据提炼人工调度经验来指导日常工作,机器学习等技术正逐渐应用于水库调控实践中。然而,仅依赖机器学习技术形成的水库调度方案,往往无法真实反映水库调度过程,使得调度经验刻画不到位。因此,本研究构建了物理机制引导的水库调度深度学习模型,以损失函数惩罚项的形式考虑水库出库流量的水量平衡约束、单调性约束、边界约束,以数据增强的方式在模型训练集与验证集中纳入稀遇洪水调度过程。研究结果表明,该模型在常规运行条件与稀遇水文条件均能有效模拟水库调度决策,与基准模型相比,该模型的模拟结果更符合水量平衡原理,有效减少负值流量,能准确模拟高值流量。该模型可为水库智慧调度的实现提供技术支撑。

Machine learning and other related technologies find increasing applications to extracting manual operation experiences from massive data in the practice of reservoir regulation.However,reservoir operation schemes solely based on machine learning fail to describe reservoir operation with enough accuracy,resulting in outliers in calculation results and a lack of operational experience.This paper constructs a deep learning model guided by the physical mechanism for reservoir operation,taking the water balance constraint,monotonicity constraint,and boundary constraint as the penalty terms of loss functions;data enhancement is used to include the factor of rare flood operations in the data sets of model training and verification.Results show this model is effective in simulating reservoir decisions for conventional operations and rare flood operations.It better satisfies the water balance equation,reduces negative flows effectively,and improves high flow simulation accuracies in comparison with the benchmark model,thus promoting the realization of intelligent reservoir operation.