In hydroelectric plants, the responsibility for the operation of the reservoirs typically lies with the national system operator, who controls the level of the reservoirs based on a stochastic problem for the economy of the potential energy available in the reservoir. However, in an emergency, the responsibility for the operation and control of the reservoir becomes the plant’s management. To have a faster decision-making process, it is important to have a forecast of water affluence in relation to the turbine capacity and use of the spillway. With the objective of evaluating the forecast increase in the level of the reservoir of a hydroelectric plant, this paper compares the use of the bagging, boosting, random subspace, bagging plus random subspace, and stacked generalization ensemble learning models to analyze this problem. The case study is based on data from a 690 MW hydroelectric plant, which has a 94 km reservoir and a 185 m high dam. The random subspace and stacking models had the best results for lower error, with a low time required for convergence in relation to the other models. The ensemble models resulted in greater accuracy for the assessed problem than long short-term memory.

在水力发电厂中，水库运营的责任通常由国家系统运营商负责，他们根据水库中可用势能的经济性随机问题控制水库的水位。然而，在紧急情况下，水库的运行和控制的责任变成了工厂的管理。为了更快地做出决策，重要的是要预测与涡轮机容量和溢洪道使用相关的水丰度。以评价某水电站水库水位预测增长为目的，本文比较了使用bagging、boosting、随机子空间、bagging加随机子空间、stacked generalization 集成学习模型来分析该问题。案例研究基于来自 690 MW 水力发电厂的数据，该发电厂拥有 94 公里长的水库和 185 米高的大坝。随机子空间和堆叠模型在较低的误差方面具有最佳结果，与其他模型相比，收敛所需的时间较短。与长期短期记忆相比，集成模型对评估问题的准确性更高。