{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:50:47Z","timestamp":1783039847586,"version":"3.54.6"},"reference-count":75,"publisher":"Wiley","issue":"1","license":[{"start":{"date-parts":[[2026,2,25]],"date-time":"2026-02-25T00:00:00Z","timestamp":1771977600000},"content-version":"vor","delay-in-days":55,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,1,1]],"date-time":"2026-01-01T00:00:00Z","timestamp":1767225600000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100007614","name":"Al Jouf University","doi-asserted-by":"publisher","award":["DGSSR-2024-02-01215"],"award-info":[{"award-number":["DGSSR-2024-02-01215"]}],"id":[{"id":"10.13039\/501100007614","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":[[2026,1]]},"abstract":"<jats:p>\n                    Accurate wind\u2010power forecasting is essential for the successful integration of renewable energy into smart city grids. Traditional long short\u2010term memory (LSTM) networks face significant challenges in parameter optimization, leading to suboptimal forecasting performance. This paper introduces a novel Bernstein dynamic horned lizard optimization algorithm (BDHLOA) to optimize LSTM parameters for wind power prediction in smart cities. BDHLOA incorporates three key enhancements: a Bernstein\u2010assisted oppositional\u2010multiple learning (BOML) strategy that improves exploration\u2013exploitation balance, Bernstein\u2010based adaptive differential (BAD) strategy for better solution refinement, and a dynamic drift search (DDS) mechanism that prevents premature convergence. The proposed BDHLOA\u2010LSTM framework is evaluated for one\u2010step\u2010ahead (10\u2010min horizon) wind\u2010power forecasting using four real\u2010world datasets from La Haute Borne wind turbines in France, under a rolling\u2010origin expanding\u2010window cross\u2010validation protocol that strictly prevents data leakage. Results demonstrate exceptional performance across all four stations, with mean\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    \u2009=\u20090.9695, RMSE\u2009=\u20090.0011, and MAE\u2009=\u20090.0007. BDHLOA\u2010LSTM reduces MAE by 94% compared to persistence forecasting and 91% compared to autoregressive AR(24) models. Against the best competing optimizer (PSO\u2010LSTM), BDHLOA\u2010LSTM achieves 42% lower MAE on average across sites. Performance is assessed using\n                    <jats:italic>R<\/jats:italic>\n                    <jats:sup>2<\/jats:sup>\n                    , RMSE, MAE, symmetric mean absolute percentage error (sMAPE), mean absolute scaled error (MASE), and prediction interval coverage probability (PICP) to capture both accuracy and calibrated uncertainty. Furthermore, the proposed model is interpreted using SHapley Additive exPlanations (SHAP) technique. SHAP analysis confirms that BDHLOA\u2010LSTM learns physically meaningful relationships dominated by wind speed statistics and direction, rather than exploiting spurious correlations. The superior accuracy and stability of BDHLOA\u2010LSTM make it highly suitable for real\u2010time grid management and sustainable energy planning in smart cities.\n                  <\/jats:p>","DOI":"10.1155\/int\/6851438","type":"journal-article","created":{"date-parts":[[2026,3,8]],"date-time":"2026-03-08T12:12:20Z","timestamp":1772971940000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Explainable Optimized LSTM Model for Sustainable Wind Power Forecasting in Smart Cities"],"prefix":"10.1155","volume":"2026","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8610-7172","authenticated-orcid":false,"given":"Abdulaziz","family":"Shehab","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9714-0717","authenticated-orcid":false,"given":"Mahmoud","family":"Abdel-Salam","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8461-4194","authenticated-orcid":false,"given":"Abdulrahman","family":"Alyami","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9489-3449","authenticated-orcid":false,"given":"Ibrahim M.","family":"El-Hasnony","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,2,25]]},"reference":[{"key":"e_1_2_12_1_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.egyr.2023.07.021"},{"key":"e_1_2_12_2_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.esr.2024.101409"},{"key":"e_1_2_12_3_2","doi-asserted-by":"publisher","DOI":"10.1016\/j.measurement.2023.112505"},{"key":"e_1_2_12_4_2","volume-title":"Share of Energy Consumption From Renewable Sources in Europe","author":"Agency E. 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