最大最小水位是计算梯级水库调度问题、水电站经济运行问题等时要考虑的重要约束条件,其精确预测能为水电站经济运行提供支持.常用的迭代计算容易误差积累,导致多时段预测结果可信度降低.选取对时间序列问题有良好处理效果的长短时记忆网络模型(LSTM),将其应用于三峡电站未来4 d最大最小坝前水位预测中,依据水量平衡预测框架构建传统预测模型;基于LSTM使用不同特征变量构建2种深度学习模型,并比较其预测效果.计算结果表明,考虑三峡库区水面线传播规律后的深度学习模型预测具有精确稳定的预测效果,99％预测绝对误差＜40 cm.
Predicting Maximum and Minimum Future Water Levels in front of Three Gorges Dam Using Deep Learning
The maximum and minimum water levels are crucial constraints in the calculation of cascade reservoir op-erations and the economic operation of hydropower stations.Traditional iterative methods for multi-period predictions lack credibility due to error accumulation.This study employs a Long Short-Term Memory(LSTM)model which is effective in handling time series problems to predict the maximum and minimum water levels of the Three Gorges Reservoir over the next four days.Two LSTM-based deep learning models incorporating different characteristic varia-bles are developed,and a conventional forecast model based on the water balance framework is also constructed for comparison.Results demonstrate that the deep learning model,which considers the propagation law of water surface profiles in the Three Gorges Reservoir area,delivers accurate and stable predictions,achieving an absolute error of less than 40 cm for 99％of the predictions.
economic operation of hydropower stationwater level predictionLSTMdeep learningneural networkThree Gorges hydropower station
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