{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T18:42:11Z","timestamp":1776537731446,"version":"3.51.2"},"reference-count":63,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T00:00:00Z","timestamp":1738281600000},"content-version":"vor","delay-in-days":30,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2025,1,1]],"date-time":"2025-01-01T00:00:00Z","timestamp":1735689600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100002850","name":"Fondo Nacional de Desarrollo Cient\u00edfico y Tecnol\u00f3gico","doi-asserted-by":"publisher","award":["1221938"],"award-info":[{"award-number":["1221938"]}],"id":[{"id":"10.13039\/501100002850","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["International Journal of Intelligent Systems"],"published-print":{"date-parts":[[2025,1]]},"abstract":"<jats:p>\n                    Renewable energy forecasting is crucial for pollution prevention, management, and long\u2010term sustainability. In response to the challenges associated with energy forecasting, the simultaneous deployment of several data\u2010processing approaches has been used in a variety of studies in order to improve the energy\u2013time\u2010series analysis, finding that, when combined with the wavelet analysis, deep learning techniques can achieve high accuracy in energy forecasting applications. Consequently, we investigate the implementation of various wavelets within the structure of a long short\u2010term memory neural network (LSTM), resulting in the new LSTM wavelet (LSTMW) neural network. In addition, and as an improvement phase, we modeled the uncertainty and incorporated it into the forecast so that systemic biases and deviations could be accounted for (LSTMW with luster: LSTMWL). The models were evaluated using data from six renewable power generation plants in Chile. When compared to other approaches, experimental results show that our method provides a prediction error within an acceptable range, achieving a coefficient of determination (\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    ) between 0.73 and 0.98 across different test scenarios, and a consistent alignment between forecasted and observed values, particularly during the first 3 prediction steps.\n                  <\/jats:p>","DOI":"10.1155\/int\/8890906","type":"journal-article","created":{"date-parts":[[2025,1,31]],"date-time":"2025-01-31T06:10:38Z","timestamp":1738303838000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Long Short\u2010Term Memory Wavelet Neural Network for Renewable Energy Generation Forecasting"],"prefix":"10.1155","volume":"2025","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1593-8109","authenticated-orcid":false,"given":"Eliana","family":"Vivas","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3047-8817","authenticated-orcid":false,"given":"H\u00e9ctor","family":"Allende-Cid","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0742-663X","authenticated-orcid":false,"given":"Lelys Bravo de","family":"Guenni","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1014-1010","authenticated-orcid":false,"given":"Aurelio F.","family":"Bariviera","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0350-6811","authenticated-orcid":false,"given":"Rodrigo","family":"Salas","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","published-online":{"date-parts":[[2025,1,31]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.3390\/e22121412"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijforecast.2005.06.006"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2010.12.008"},{"key":"e_1_2_12_4_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2017.08.066"},{"key":"e_1_2_12_5_2","article-title":"A Two-Step Approach to Solar Power Generation Prediction Based on Weather Data Using Machine Learning","volume":"11","author":"Kim S. 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