Because of the intrinsic complexity and chaotic nature of wind speed time series, an appropriate model for accurately forecasting the moving tendency is required. In this paper, we propose an evolutionary dendritic neuron model (EDNM) to carry out wind speed forecasting. The model is trained via adaptive differential evolution with the linear population size reduction (L-SHADE) algorithm. Specifically, a mutual-information-based approach and the false nearest neighbours method are used to calculate the time delay and embedding dimensions, respectively. Then, the phase space of the wind speed time series is reconstructed based on the time delay and embedding dimensions, and the characteristics are analysed. The maximum Lyapunov exponent is applied to confirm the chaotic properties of the wind speed time series. Finally, EDNM trained by L-SHADE is used to predict the wind speed for Sotavento, which is located near Galicia, Spain. This study is the first, to the best of our knowledge, to use a dendritic neuron model to implement such real-world prediction. Extensive experimental results show that the proposed EDNM can perform better than other state-of-the-art models in terms of different assessment criteria. Therefore, the proposed method has high potential for practical applications in electric power systems.

由于风速时间序列的内在复杂性和混沌性质，需要一种用于准确预测运动趋势的合适模型。在本文中，我们提出了一种进化树突神经元模型（EDNM）来进行风速预测。该模型通过使用线性种群规模缩减（L-SHADE）算法的自适应差分进化进行训练。具体而言，基于互信息的方法和错误的最近邻居方法分别用于计算时间延迟和嵌入维数。然后，基于时延和嵌入维数，重建风速时间序列的相空间，并对其特性进行分析。应用最大李雅普诺夫指数来确认风速时间序列的混沌特性。最后，由L-SHADE培训的EDNM用于预测位于西班牙加利西亚附近的Sotavento的风速。就我们所知，这项研究是首次使用树突神经元模型来实现这种真实世界的预测。大量的实验结果表明，在不同的评估标准方面，所提出的EDNM可以比其他最新模型表现更好。因此，提出的方法在电力系统中的实际应用具有很高的潜力。大量的实验结果表明，在不同的评估标准方面，所提出的EDNM可以比其他最新模型表现更好。因此，提出的方法在电力系统中的实际应用具有很高的潜力。大量的实验结果表明，在不同的评估标准方面，所提出的EDNM可以比其他最新模型表现更好。因此，提出的方法在电力系统中的实际应用具有很高的潜力。