{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T22:24:09Z","timestamp":1776205449151,"version":"3.50.1"},"reference-count":73,"publisher":"MDPI AG","issue":"21","license":[{"start":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T00:00:00Z","timestamp":1634860800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The accuracy of a predictive system is critical for predictive maintenance and to support the right decisions at the right times. Statistical models, such as ARIMA and SARIMA, are unable to describe the stochastic nature of the data. Neural networks, such as long short-term memory (LSTM) and the gated recurrent unit (GRU), are good predictors for univariate and multivariate data. The present paper describes a case study where the performances of long short-term memory and gated recurrent units are compared, based on different hyperparameters. In general, gated recurrent units exhibit better performance, based on a case study on pulp paper presses. The final result demonstrates that, to maximize the equipment availability, gated recurrent units, as demonstrated in the paper, are the best options.<\/jats:p>","DOI":"10.3390\/en14216958","type":"journal-article","created":{"date-parts":[[2021,10,22]],"date-time":"2021-10-22T09:18:09Z","timestamp":1634894289000},"page":"6958","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":131,"title":["Comparing LSTM and GRU Models to Predict the Condition of a Pulp Paper Press"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-1953-7268","authenticated-orcid":false,"given":"Baldu\u00edno C\u00e9sar","family":"Mateus","sequence":"first","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"},{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 62001-001 Covilh\u00e3, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4313-7966","authenticated-orcid":false,"given":"Mateus","family":"Mendes","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Coimbra, Polytechnic of Coimbra, 3045-093 Coimbra, Portugal"},{"name":"Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9694-8079","authenticated-orcid":false,"given":"Jos\u00e9 Torres","family":"Farinha","sequence":"additional","affiliation":[{"name":"Institute of Systems and Robotics, University of Coimbra, 3004-531 Coimbra, Portugal"},{"name":"Centre for Mechanical Engineering, Materials and Processes\u2014CEMMPRE, University of Coimbra, 3030-788 Coimbra, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1697-055X","authenticated-orcid":false,"given":"Rui","family":"Assis","sequence":"additional","affiliation":[{"name":"EIGeS\u2014Research Centre in Industrial Engineering, Management and Sustainability, Lus\u00f3fona University, Campo Grande, 376, 1749-024 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8737-6999","authenticated-orcid":false,"given":"Ant\u00f3nio Marques","family":"Cardoso","sequence":"additional","affiliation":[{"name":"CISE\u2014Electromechatronic Systems Research Centre, University of Beira Interior, Cal\u00e7ada Fonte do Lameiro, 62001-001 Covilh\u00e3, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,10,22]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bousdekis, A., Lepenioti, K., Apostolou, D., and Mentzas, G. 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