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

Alejandro Morales-Hernández, Gonzalo Nápoles, Agnieszka Jastrzebska, Yamisleydi Salgueiro, Koen Vanhoof

Research output: Chapter in Book/Report/Conference proceedingConference contributionScientificpeer-review

Abstract

The amount of data generated by windmill farms makes online learning the most viable forecasting strategy. However, updating a forecasting model with a new batch of data is often very expensive when using recurrent neural network models. Long Short-term Cognitive Networks (LSTCNs) are a novel gated neural network consisting 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 simulations using a case study involving four windmills showed that our approach reported the lowest forecasting errors and training time with respect to traditional models.
Original languageEnglish
Title of host publicationJoint International Scientific Conferences on AI and Machine Learning BNAIC/BeNeLearn 2022
Number of pages3
Publication statusPublished - 2022

Keywords

  • Long Short-term Cognitive Network
  • Recurrent Neural Network
  • Time Series Forecasting

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