Because wind has a high volatility and the respective energy produced cannot be stored on a large scale because of excessive costs, it is of utmost importance to be able to forecast wind power generation with the highest accuracy possible. The aim of this paper is to compare 1‐h‐ahead wind power forecasts performance using artificial intelligence‐based methods, such as artificial neural networks (ANNs), adaptive neural fuzzy inference system (ANFIS), and radial basis function network (RBFN). The latter was implemented using three different learning algorithms: stochastic gradient descent (SGD), hybrid, and orthogonal least squares (OLS). The application dataset is the injected wind power in the Portuguese power systems throughout the years 2010–2014. The network architecture optimization and the learning algorithms are presented. An initial data analysis showed data seasonality; therefore, the wind power forecasts were performed according to the seasons of the year. The results showed that ANFIS was the best performer method, and ANN and RBFN‐OLS also showed strong performances. RBFN‐Hybrid and RBFN‐SGD performed poorly. In general, all methods outperformed persistence.

中文翻译：

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人工智能方法在葡萄牙风电预测中的比较性能

因为风具有高波动性，并且由于过高的成本而不能大规模地存储所产生的各个能量，所以能够以尽可能高的精度预测风能发电至关重要。本文的目的是使用基于人工智能的方法（例如，人工神经网络（ANN），自适应神经模糊推理系统（ANFIS）和径向基函数网络（RBFN））来比较1小时风电预测性能。后者使用三种不同的学习算法来实现：随机梯度下降（SGD），混合和正交最小二乘（OLS）。应用程序数据集是2010-2014年间葡萄牙电力系统中注入的风能。提出了网络架构优化和学习算法。初步数据分析显示数据季节性；因此，风力发电量预测是根据一年中的季节进行的。结果表明，ANFIS是表现最好的方法，而ANN和RBFN-OLS也表现出出色的性能。RBFN‐Hybrid和RBFN‐SGD的效果较差。通常，所有方法都优于持久性。