基于LSTM神经网络的水电站短期水位预测方法

针对常规水位预测方法信息挖掘能力不足和启发式算法机理不明确等缺点,提出了一种基于长短时记忆(long short-term memory, LSTM)网络的水位预测方法。该方法采用水位和出力等直接监测数据,避免了出入库流量等间接计算值带来的二次误差,进而提升水位预测的准确率;采用梯度下降法与Broyden-Fletcher-Goldfarb-Shanno (BFGS)算法相结合训练模型,Wolfe-Powell线搜索方法选取步长,提高模型收敛速率。将该方法用于葛洲坝水电站的上下游水位预测,结果表明,该方法能够实现下游水位连续6 h和上游水位连续3 h的准确预测,具有较高的预测精度和实用性,为葛洲坝水库的实时调度提供了技术支撑。

Short-term water level prediction method for hydropower station based on LSTM neural network

In order to overcome the shortcomings such as insufficient information mining capability of conventional water level prediction methods and unclear mechanism of heuristic algorithms, an water level prediction method based on long short-term memory(LSTM)neural network is proposed. Direct monitoring data such as water level and unit output are used in this method and the middle errors caused by the indirect calculation of the outflow and inflow can be avoided, which improves the accuracy of water level prediction. A hybrid method based on the gradient descent algorithm and Broyden-Fletcher-Goldfarb-Shanno(BFGS)algorithm is used to train the model, and the step length is determined by the Wolfe-Powell line search method to accelerate convergence. The proposed method is used to predict the water level at the upstream and downstream of the Gezhouba Hydropower Station. The results show that this method can continuously predict the downstream water level for 6 hours and the upstream water level for 3 hours with high accuracy, providing technical support for the real-time scheduling of the Gezhouba Reservoir.