基于SHAP-XGBoost混合模型的梯级水电站流量动态滞时研究

Dynamic hysteresis of cascade hydropower station discharge based on SHAP-XGBoost hybrid model

水流滞时是影响梯级电站短期优化调度的重要因素。如何构建流量动态滞时模型以精确描述梯级水电上下游水力联系,成为水电短期优化调度中亟需解决的问题。尝试引入极端梯度提升树算法(eXtreme gradient boosting,XGBoost)作为表现梯级水电站间流量动态滞时的工具。首先利用沙普利加性解释(Shapley addictive explanation,SHAP)评估各输入特征重要性,并从中选择最优特征集。其次建立了一种基于极端梯度提升树的动态滞时模型,利用网格搜索和交叉验证算法对模型参数进行选取。最后基于实际案例与人工神经网络、支持向量回归及固定滞时模型进行对比实验。结果表明,所构建的SHAP-XGBoost模型能更准确地模拟水电站间动态滞时关系,与传统模型相比平均预测精度至少提高了18%,且与实测序列的变化趋势匹配程度最佳。同时证明了输入特征筛选的加入能够使模型精度进一步提高。

Water hysteresis is an important factor affecting the short-term optimal dispatch of cascade hydropower stations.How to construct a dynamic hysteresis model to accurately describe the upstream and downstream hydraulic connections of cascade hydropower has become an urgent problem to be solved in the short-term optimal dispatch of hydropower.This paper attempts to introduce the eXtreme Gradient Boosting(XGBoost)algorithm as a tool to express the dynamic water hysteresis between cascade hydropower stations.First,it uses the Shapley addictive explanation(SHAP)to evaluate the importance of each input feature and select the optimal feature set.Second,a dynamic hysteresis model based on an extreme gradient lifting tree is established.Grid search and cross-validation algorithms are used to select the model parameters.Finally,based on an actual case,comparative experiments with artificial neural networks,support vector regression and a fixed time-lag model are carried out.The results show that the constructed SHAP-XGBoost model can simulate the dynamic hysteresis relationship between hydropower stations more accurately,the average prediction accuracy of the proposed model is improved by at least 18%compared with other models,and it can best match the change trend of the measured sequence.It is also proved that the addition of input feature screening can further improve the model accuracy.