Forecasting windmill time series is often the basis of other processes such as anomaly detection, health monitoring, or maintenance scheduling. The amount of data generated on windmill farms makes online learning the most viable strategy to follow. Such settings require retraining the model each time a new batch of data is available. However, update the model with the new information is often very expensive to perform using traditional Recurrent Neural Networks (RNNs). In this paper, we use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. These recently introduced neural systems consist of chained Short-term Cognitive Network blocks, each processing a temporal data chunk. The learning algorithm of these blocks is based on a very fast, deterministic learning rule that makes LSTCNs suitable for online learning tasks. The numerical simulations using a case study with four windmills showed that our approach reported the lowest forecasting errors with respect to a simple RNN, a Long Short-term Memory, a Gated Recurrent Unit, and a Hidden Markov Model. What is perhaps more important is that the LSTCN approach is significantly faster than these state-of-the-art models.

翻译：预测风车时间序列往往是其他过程的基础,例如异常点检测、健康监测或维护时间安排。风车农场生成的数据数量使在线学习成为最可行的策略。这种设置每次有新一批数据时都需要对模型进行再培训。然而,使用传统的常态神经网络(NealNetworks)更新模型往往非常昂贵。在本文中,我们使用长期短期认知网络(LSTCNs)来预测在线环境中的风车时间序列。这些最近引入的神经系统由链式短期认知网络块组成,每个处理时空数据块。这些区块的学习算法基于非常快速、决定性的学习规则,该规则使LSTCNs适合在线学习任务。使用四部风车的案例研究进行的数字模拟表明,我们的方法报告了一个简单的RNN、长短期内存、Greded经常单元和隐藏式Markov模型的最低预测错误。也许更重要的是,LSTCN方法比这些状态模型要快得多。