Optimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecasting

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dc.authoridBacanin, Nebojsa/0000-0002-2062-924X
dc.authoridZivkovic, Miodrag/0000-0002-4351-068X
dc.authoridPavlov-Kagadejev, Marijana/0000-0003-1090-6351
dc.authorwosidBacanin, Nebojsa/L-5328-2019
dc.authorwosidZivkovic, Miodrag/AFC-8832-2022
dc.contributor.authorPavlov-Kagadejev, Marijana
dc.contributor.authorJovanovic, Luka
dc.contributor.authorBacanin, Nebojsa
dc.contributor.authorDeveci, Muhammet
dc.contributor.authorZivkovic, Miodrag
dc.contributor.authorTuba, Milan
dc.contributor.authorStrumberger, Ivana
dc.date.accessioned2024-05-19T14:43:04Z
dc.date.available2024-05-19T14:43:04Z
dc.date.issued2024
dc.departmentİstinye Üniversitesien_US
dc.description.abstractPower supply from renewable energy is an important part of modern power grids. Robust methods for predicting production are required to balance production and demand to avoid losses. This study proposed an approach that incorporates signal decomposition techniques with Long Short-Term Memory (LSTM) neural networks tuned via a modified metaheuristic algorithm used for wind power generation forecasting. LSTM networks perform notably well when addressing time-series prediction, and further hyperparameter tuning by a modified version of the reptile search algorithm (RSA) can help improve performance. The modified RSA was first evaluated against standard CEC2019 benchmark instances before being applied to the practical challenge. The proposed tuned LSTM model has been tested against two wind production datasets with hourly resolutions. The predictions were executed without and with decomposition for one, two, and three steps ahead. Simulation outcomes have been compared to LSTM networks tuned by other cutting-edge metaheuristics. It was observed that the introduced methodology notably exceed other contenders, as was later confirmed by the statistical analysis. Finally, this study also provides interpretations of the best-performing models on both observed datasets, accompanied by the analysis of the importance and impact each feature has on the predictions.en_US
dc.description.sponsorshipScience Fund of the Republic of Serbia [7502]en_US
dc.description.sponsorshipThis research was supported by the Science Fund of the Republic of Serbia, Grant No. 7502, Intelligent Multi-Agent Control and Optimization applied to Green Buildings and Environmental Monitoring Drone Swarms - ECOSwarm.en_US
dc.identifier.doi10.1007/s10462-023-10678-y
dc.identifier.issn0269-2821
dc.identifier.issn1573-7462
dc.identifier.issue3en_US
dc.identifier.scopus2-s2.0-85184952196en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org10.1007/s10462-023-10678-y
dc.identifier.urihttps://hdl.handle.net/20.500.12713/5318
dc.identifier.volume57en_US
dc.identifier.wosWOS:001161058700003en_US
dc.identifier.wosqualityN/Aen_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.language.isoenen_US
dc.publisherSpringeren_US
dc.relation.ispartofArtificial Intelligence Reviewen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.snmz20240519_kaen_US
dc.subjectLong Short-Term Memory Networksen_US
dc.subjectMetaheuristics Optimizationen_US
dc.subjectReptile Search Algorithmen_US
dc.subjectShapley Additive Explanationsen_US
dc.subjectWind Power Generationen_US
dc.titleOptimizing long-short-term memory models via metaheuristics for decomposition aided wind energy generation forecastingen_US
dc.typeArticleen_US

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