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http://hdl.handle.net/1942/36204
Title: | Online learning of windmill time series using Long Short-term Cognitive Networks | Authors: | MORALES HERNANDEZ, Alejandro NAPOLES RUIZ, Gonzalo Jastrzebska, Agnieszka Salgueiro, Yamisleydi VANHOOF, Koen |
Issue Date: | 2022 | Publisher: | PERGAMON-ELSEVIER SCIENCE LTD | Source: | Expert Systems with Applications, 205 , (Art. N° 117721) | 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 by 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, updating the model with new information is often very expensive when 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 involving 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. | Keywords: | long short-term cognitive network;recurrent neural network;multivariate time series;forecasting | Document URI: | http://hdl.handle.net/1942/36204 | ISSN: | 0957-4174 | e-ISSN: | 1873-6793 | DOI: | 10.1016/j.eswa.2022.117721 | ISI #: | 000832961500005 | Datasets of the publication: | https://opendata-renewables.engie.com/explore/index | Rights: | This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). | Category: | A1 | Type: | Journal Contribution | Validations: | ecoom 2023 |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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revised_manuscript_CLEAN.pdf | Non Peer-reviewed author version | 1.32 MB | Adobe PDF | View/Open |
1-s2.0-S0957417422010065-main.pdf | Published version | 1.42 MB | Adobe PDF | View/Open |
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