This paper proposes a powerful and accurate prediction method for modeling wind turbine output power considering uncertainty effects. It develops a probabilistic prediction method to make intervals around the forecast point samples. Due to the instable nature of the neural networks, several models with different classes are first trained, and then a combinatorial interval is constructed based on all their results. Through this idea, some lower and upper bounds are produced which can capture the uncertainty effects. In order to train the neural networks and adjust their setting parameters, a new optimization method based on the social spider optimization search (SOS) algorithm is presented which is equipped with some modification methods for increasing its capabilities. This increases the training robustness, thus resulting in stable but accurate algorithms. The experimental results of some wind turbines are used to assess the performance of the proposed method.

中文翻译：

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基于组合神经网络的风力发电机组功率短期鲁棒预测

本文提出了一种强大而准确的预测方法，用于模拟考虑不确定性影响的风力涡轮机输出功率。它开发了一种概率预测方法来在预测点样本周围设置区间。由于神经网络的不稳定特性，首先训练几个不同类别的模型，然后根据它们的所有结果构建一个组合区间。通过这个想法，产生了一些可以捕捉不确定性影响的下限和上限。为了训练神经网络并调整其设置参数，提出了一种基于社交蜘蛛优化搜索（SOS）算法的新优化方法，该方法配备了一些修改方法以提高其能力。这增加了训练的鲁棒性，从而产生稳定但准确的算法。一些风力涡轮机的实验结果用于评估所提出方法的性能。