Streamflow series forecasting composes a fundamental step in planning electric energy production for hydroelectric plants. In Brazil, such plants produce almost 70% of the total energy. Therefore, it is of great importance to improve the quality of streamflow series forecasting by investigating state-of-the-art time series forecasting algorithms. To this end, this work proposes the development of ensembles of unorganized machines, namely Extreme Learning Machines (ELMs) and Echo State Networks (ESNs). Two primary contributions are proposed: (1) a new training logic for ESNs that enables the application of bootstrap aggregation (bagging); and (2) the employment of multi-objective optimization to select and adjust the weights of the ensemble’s base models, taking into account the trade-off between bias and variance. Experiments are conducted on streamflow series data from five real-world Brazilian hydroelectric plants, namely those in Sobradinho, Serra da Mesa, Jiraú, Furnas and Água Vermelha. The statistical results for four different prediction horizons (1, 3, 6, and 12 months ahead) indicate that the ensembles of unorganized machines achieve better results than autoregressive (AR) models in terms of the Nash–Sutcliffe model efficiency coefficient (NSE), root mean squared error (RMSE), coefficient of determination (R${}^2$), and RMSE-observations standard deviation ratio (RSR). In such results, the ensembles with ESNs and the multi-objective optimization design procedure achieve the best scores.

流量系列预报是规划水力发电厂电能生产的基本步骤。在巴西，此类植物产生的能量几乎占总能量的70％。因此，通过研究最新的时间序列预测算法来提高流量序列预测的质量至关重要。为此，这项工作建议开发无组织机器的集合，即极限学习机器（ELM）和回声状态网络（ESN）。提出了两个主要的贡献：（1）一种用于ESN的新训练逻辑，该逻辑使得能够应用引导聚合（装袋）；（2）考虑到偏差和方差之间的折衷，采用多目标优化来选择和调整集成基础模型的权重。索布拉迪纽，埃什特雷拉梅萨，Jiraú，弗纳斯和阿瓜Vermelha。四个不同预测范围（分别为1、3、6和12个月）的统计结果表明，就纳什-萨特克利夫模型效率系数（NSE）而言，无组织机器的集成比自回归（AR）模型获得更好的结果，均方根误差（RMSE），确定系数（R${}^2$），以及RMSE观测标准差比（RSR）。在这样的结果中，具有ESN的合奏和多目标优化设计过程获得了最佳分数。