{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,21]],"date-time":"2026-09-21T11:07:03Z","timestamp":1789988823410,"version":"4.0.1"},"update-to":[{"DOI":"10.12688\/f1000research.172121.1","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000}},{"DOI":"10.12688\/f1000research.172121.2","type":"new_version","label":"New version","source":"publisher","updated":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000}}],"reference-count":37,"publisher":"F1000 Research Ltd","license":[{"start":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T00:00:00Z","timestamp":1788393600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/100023613","name":"Academic Research Institute in Iraq","doi-asserted-by":"publisher","id":[{"id":"10.13039\/100023613","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["f1000research.com"],"crossmark-restriction":false},"short-container-title":["F1000Res"],"abstract":"<ns3:p>\n                    Background Solar irradiance is a nonlinear and intermittent function, which makes accurate forecasting of solar power generation a challenge. The high variability of meteorological conditions is not well represented by conventional atmospheric models, thus hampering forecasting skill and model robustness. In this work, an advanced hybridization of multi-population cuckoo search (HMPCS) algorithm with machine learning (ML) methods is developed to enhance the predicated performance of photovoltaic (PV) power forecasting with more reliability. Methods In this study, a hybrid modeling framework is proposed, called HMPCS\u2013ML framework which captures the global search capacity of HMPCS and predictive power of sophisticated ML models (Long Short-Term Memory (LSTM), Light Gradient Boosting Machine (LightGBM)). Optimizing hyperparameters by balancing exploration and exploitation, the algorithm runs on multi-populations through L\u00e9vy flight randomization. Interpolation, normalization, and temporal windowing were utilized to preprocess synthetic meteorological and irradiance datasets. We evaluated the framework by comparing commonly used statistical measures (MAE, RMSE, MAPE, R\n                    <ns3:sup>2<\/ns3:sup>\n                    ). Results Moreover, experimental analyses showed that HMPCS\u2013ML models significantly outperformed baseline approaches (Grid Search and Particle Swarm Optimization (PSO)). Results showed that the optimized LSTM+HMPCS model outperformed other models in terms of lowest RMSE (0.139) and highest R\n                    <ns3:sup>2<\/ns3:sup>\n                    (0.93), reflecting the LSTM model\u2019s good fit with practical observations and generalization ability. The optimal LightGBM+HMPCS variant also proved to be consistently better, with reduced error (23% lower than unoptimized models). Conclusions In this regard, the HMPCS\u2013ML framework is a powerful and efficient solution for the optimization of Photovoltaic (PV) power forecasting, improving the predictive performance and calculation efficiency. This research shows the potential of hybrid metaheuristic\u2013ML integration for renewable energy prediction and smart-grid applications in general and indicates further extensions to multi-objective and Transformer-based architectures.\n                  <\/ns3:p>","DOI":"10.12688\/f1000research.172121.3","type":"journal-article","created":{"date-parts":[[2026,9,3]],"date-time":"2026-09-03T09:09:06Z","timestamp":1788426546000},"page":"128","update-policy":"https:\/\/doi.org\/10.12688\/f1000research.crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Enhancing Solar Power Forecasting Accuracy Using HMPCS and Machine Learning Techniques: An Applied Study"],"prefix":"10.12688","volume":"15","author":[{"given":"Asmaa","family":"Abdul-Hussein Aziz","sequence":"first","affiliation":[{"name":"Department of Mathematics Baghdad 00964 University of Baghdad College of Science"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4054-9586","authenticated-orcid":false,"given":"Iraq T.","family":"Abbas","sequence":"additional","affiliation":[{"name":"Department of Mathematics Baghdad 00964 University of Baghdad College of Science"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"2560","published-online":{"date-parts":[[2026,9,3]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"6744","DOI":"10.1038\/s41598-024-56751-6","article-title":"A short-term forecasting method for photovoltaic power generation based on the TCN\u2013ECA Net\u2013GRU hybrid model.","volume":"14","author":"X Xiang","year":"2024","journal-title":"Sci. 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We sincerely appreciate your valuable comments and constructive suggestions throughout the review process, which have helped us improve the quality and clarity of the manuscript. \n<b>Best regards,<\/b> The Authors","URL":"https:\/\/f1000research.com\/articles\/15-128\/v3#referee-comment-17193","order":1,"name":"referee-comment-17193","label":"Referee Comment","group":{"name":"article-reports","label":"Article Reports"}},{"value":"10.5256\/f1000research.208113.r517524, Bin Du, Southeast University, Nanjing, Jiangsu, 14 Sep 2026, version 3, indexed","URL":"https:\/\/f1000research.com\/articles\/15-128\/v3#referee-response-517524","order":2,"name":"referee-response-517524","label":"Referee Report","group":{"name":"article-reports","label":"Article Reports"}},{"value":"This research was financially supported by the University of Fallujah, Iraq, through its academic research funding program. The support covered data analysis, computational resources, and publication preparation.\nThe authors gratefully acknowledge the University of Fallujah for providing the facilities and financial assistance that enabled the completion of this study. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.","order":3,"name":"grant-information","label":"Grant Information"},{"value":"This is an open access article distributed under the terms of the Creative Commons Attribution Licence, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.","order":0,"name":"copyright-info","label":"Copyright"}]}}