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首页> 外文期刊>Energy sources >Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method
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Forecasting diversion type hydropower plant generations using an artificial bee colony based extreme learning machine method

机译：基于人工蜂殖民地的极端学习机方法预测转移型水电站

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摘要

In this study, a hybrid method based on extreme learning machine (ELM) method and artificial bee colony (ABC) algorithm was proposed to forecast small hydropower plant generations. The input weights and biases of ELM were optimized by ABC algorithm to achieve more accurate forecasting results. The forecasting performance of the proposed method was compared with benchmark methods, namely backpropagation-based artificial neural network (ANN), radial basis function-based ANN, and long short-term memory. The experimental results verified that the proposed method significantly outperformed the benchmark methods. Specially, when the proposed method was compared with ELM, the improvement percentages in correlation coefficient, root mean square error, and mean absolute error values were calculated as being 6.20%-29.08%-26.29% for 14 days ahead and 5.47%-24.42%-20.33% for 21 days ahead, respectively.
机译：在该研究中，提出了一种基于极端学习机（ELM）方法和人造蜂菌落（ABC）算法的混合方法，以预测小型水电厂。 通过ABC算法优化ELM的输入权重和偏差，以实现更准确的预测结果。 将所提出的方法的预测性能与基准方法，即基于背交的人工神经网络（ANN），径向基函数的ANN和长短期记忆的基于反正桥的方法。 实验结果证实，该方法明显优于基准方法。 特别地，当提出的方法与ELM进行比较时，相关系数，根均方误差和平均绝对误差值的改善百分比计算为未来14天的6.20％-29.08％-26.29％，5.47％-24.42％ 分别为-20.33％，分别提前21天。

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