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http://hdl.handle.net/1942/38756
Title: | Encore abstract "Online learning of windmill time series using Long Short-term Cognitive Networks" | Authors: | MORALES HERNANDEZ, Alejandro NAPOLES RUIZ, Gonzalo Jastrz¸ebska, Agnieszka Salgueiro Sicilia, Yamisleydi VANHOOF, Koen |
Issue Date: | 2022 | Status: | Early view | Abstract: | The amount of data generated by windmill farms make 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 networks 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. | Document URI: | http://hdl.handle.net/1942/38756 | Category: | C2 | Type: | Conference Material |
Appears in Collections: | Research publications |
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File | Description | Size | Format | |
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BNAIC_2022 v2.pdf Restricted Access | Conference material | 207.67 kB | Adobe PDF | View/Open Request a copy |
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