  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.
