ABSTRACT

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.

摘要

在这项研究中，提出了一种基于极限学习机（ELM）方法和人工蜂群（ABC）算法的混合方法来预测小型水力发电厂的发电量。通过ABC算法对ELM的输入权重和偏差进行了优化，以实现更准确的预测结果。将该方法的预测性能与基于反向传播的人工神经网络，基于径向基函数的神经网络和长短期记忆的基准方法进行了比较。实验结果证明，该方法明显优于基准方法。特别地，当将所提出的方法与ELM进行比较时，提前14天和5天的相关系数，均方根误差和平均绝对误差值的改进百分比计算为6.20％-29.08％-26.29％。