Online learning of windmill time series using Long Short-term Cognitive Networks

• URL: http://arxiv.org/abs/2107.00425v1
• Date: Thu, 1 Jul 2021 13:13:24 GMT
• Title: Online learning of windmill time series using Long Short-term Cognitive Networks
• Authors: Alejandro Morales-Hern\'andez, Gonzalo N\'apoles, Agnieszka Jastrzebska, Yamisleydi Salgueiro, Koen Vanhoof
• Abstract summary: The amount of data generated on windmill farms makes online learning the most viable strategy to follow. We use Long Short-term Cognitive Networks (LSTCNs) to forecast windmill time series in online settings. 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.
• Score: 58.675240242609064
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: 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.

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