{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,7,30]],"date-time":"2025-07-30T13:20:05Z","timestamp":1753881605573,"version":"3.41.2"},"reference-count":28,"publisher":"World Scientific Pub Co Pte Ltd","issue":"08","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["J CIRCUIT SYST COMP"],"published-print":{"date-parts":[[2025,5,30]]},"abstract":"<jats:p> Solar energy can be considered as an alternate solution to conventional energy sources. Short-term Photovoltaic (PV) Power Generation (PVPG) prediction methods are essential stabilize power integration among PV and smart grids. The PVPG generation process is highly dependent on climatic conditions and therefore high intermittent. Highly accurate PVPG prediction of PVPG acts based on the generation, transmission and dispersion of electricity, confirming the stability and dependability of power system. The current progress of Machine Learning (ML) and Deep Learning (DL) approaches enables for designing of accurate PVPG prediction models. In this view, this paper develops a new Badger Optimization with Deep Learning Enabled PV Power Generation Predictive (HBODL-PVPGP) model. The presented HBODL-PVPGP model enables to forecast of the PVPG process. To accomplish this, the HBODL-PVPGP model initially investigates the features depending upon intrinsic characteristics earlier in the learning process. In addition, the Bidirectional Gated Recurrent Unit (BiGRU) model is implemented for forecasting process. The performance of the BiGRU model can be improvised by the design of HBO-based hyperparameter tuning procedure. For ensuring the enhanced performance of the HBODL-PVPGP model, an extensive range of experimental study was effectuated and the results were investigated under various factors. The result highlighted the precipitated performance of the HBODL-PVPGP procedure on the current algorithms. <\/jats:p>","DOI":"10.1142\/s0218126624502207","type":"journal-article","created":{"date-parts":[[2024,1,31]],"date-time":"2024-01-31T05:10:37Z","timestamp":1706677837000},"source":"Crossref","is-referenced-by-count":0,"title":["Nature-Inspired Optimization with Deep Learning-Enabled Sustainable Predictive Model for Intelligent Systems"],"prefix":"10.1142","volume":"34","author":[{"ORCID":"https:\/\/orcid.org\/0009-0008-7574-1205","authenticated-orcid":false,"given":"Shan","family":"Ge","sequence":"first","affiliation":[{"name":"Graduate College for Engineers, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0000-3865-7559","authenticated-orcid":false,"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Qi An Xin Technology Group Inc., Beijing 100032, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-8426-1976","authenticated-orcid":false,"given":"Yong","family":"Liu","sequence":"additional","affiliation":[{"name":"Qi An Xin Technology Group Inc., Beijing 100032, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0009-0006-2892-4055","authenticated-orcid":false,"given":"Jian","family":"Kong","sequence":"additional","affiliation":[{"name":"Qi An Xin Technology Group Inc., Beijing 100032, P. R. China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0111-1023","authenticated-orcid":false,"given":"Jinqiao","family":"Shi","sequence":"additional","affiliation":[{"name":"School of Cyberspace Security, Beijing University of Posts and Telecommunications, Beijing 100876, P. R. 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