{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,20]],"date-time":"2026-07-20T13:57:06Z","timestamp":1784555826705,"version":"3.55.0"},"reference-count":97,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,3,4]],"date-time":"2023-03-04T00:00:00Z","timestamp":1677888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Romanian Ministry of Research and Innovation, CCCDI \u2013 UEFISCDI","award":["178PCE\/2021"],"award-info":[{"award-number":["178PCE\/2021"]}]},{"name":"Romanian Ministry of Research and Innovation, CCCDI \u2013 UEFISCDI","award":["PN-III-P4-ID-PCE-2020-0788"],"award-info":[{"award-number":["PN-III-P4-ID-PCE-2020-0788"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Axioms"],"abstract":"<jats:p>As solar energy generation has become more and more important for the economies of numerous countries in the last couple of decades, it is highly important to build accurate models for forecasting the amount of green energy that will be produced. Numerous recurrent deep learning approaches, mainly based on long short-term memory (LSTM), are proposed for dealing with such problems, but the most accurate models may differ from one test case to another with respect to architecture and hyperparameters. In the current study, the use of an LSTM and a bidirectional LSTM (BiLSTM) is proposed for dealing with a data collection that, besides the time series values denoting the solar energy generation, also comprises corresponding information about the weather. The proposed research additionally endows the models with hyperparameter tuning by means of an enhanced version of a recently proposed metaheuristic, the reptile search algorithm (RSA). The output of the proposed tuned recurrent neural network models is compared to the ones of several other state-of-the-art metaheuristic optimization approaches that are applied for the same task, using the same experimental setup, and the obtained results indicate the proposed approach as the better alternative. Moreover, the best recurrent model achieved the best results with R2 of 0.604, and a normalized MSE value of 0.014, which yields an improvement of around 13% over traditional machine learning models.<\/jats:p>","DOI":"10.3390\/axioms12030266","type":"journal-article","created":{"date-parts":[[2023,3,6]],"date-time":"2023-03-06T02:28:34Z","timestamp":1678069714000},"page":"266","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":89,"title":["Metaheuristic-Based Hyperparameter Tuning for Recurrent Deep Learning: Application to the Prediction of Solar Energy Generation"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5917-1857","authenticated-orcid":false,"given":"Catalin","family":"Stoean","sequence":"first","affiliation":[{"name":"Department of Computer Science, University of Craiova, A.I.Cuza, 13, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4351-068X","authenticated-orcid":false,"given":"Miodrag","family":"Zivkovic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9292-6318","authenticated-orcid":false,"given":"Aleksandra","family":"Bozovic","sequence":"additional","affiliation":[{"name":"Academy of Applied Technical Studies, Katarine Ambrozic 3, 11000 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2062-924X","authenticated-orcid":false,"given":"Nebojsa","family":"Bacanin","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9702-7554","authenticated-orcid":false,"given":"Roma","family":"Strulak-W\u00f3jcikiewicz","sequence":"additional","affiliation":[{"name":"Faculty of Economics and Transport Engineering, Maritime University of Szczecin, Wa\u0142y Chrobrego 1\/2, 70-500 Szczecin, Poland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5511-2531","authenticated-orcid":false,"given":"Milos","family":"Antonijevic","sequence":"additional","affiliation":[{"name":"Faculty of Informatics and Computing, Singidunum University, Danijelova 32, 11010 Belgrade, Serbia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9849-5712","authenticated-orcid":false,"given":"Ruxandra","family":"Stoean","sequence":"additional","affiliation":[{"name":"Department of Computer Science, University of Craiova, A.I.Cuza, 13, 200585 Craiova, Romania"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1973","DOI":"10.1016\/j.egyr.2020.07.020","article-title":"A critical review of comparative global historical energy consumption and future demand: The story told so far","volume":"6","author":"Ahmad","year":"2020","journal-title":"Energy Rep."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"1170","DOI":"10.1016\/j.rser.2016.09.137","article-title":"Opportunities, barriers and issues with renewable energy development\u2014A discussion","volume":"69","author":"Sen","year":"2017","journal-title":"Renew. 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