Intelligent wild geese algorithm with deep learning driven short term load forecasting for sustainable energy management in microgrids

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dc.authoridSenthilkumar, N/0000-0002-2441-1061
dc.authorwosidSenthilkumar, N/I-3860-2019
dc.authorwosidGürel, Ali Etem/HKF-0948-2023
dc.contributor.authorDeepanraj, B.
dc.contributor.authorSenthilkumar, N.
dc.contributor.authorJarin, T.
dc.contributor.authorGürel, Ali Etem
dc.contributor.authorSundar, L. Syam
dc.contributor.authorAnand, A. Vivek
dc.date.accessioned2023-07-26T11:51:08Z
dc.date.available2023-07-26T11:51:08Z
dc.date.issued2022
dc.departmentDÜ, Mühendislik Fakültesi, Makine Mühendisliği Bölümüen_US
dc.departmentDÜ, Mühendislik Fakültesi, Elektrik-Elektronik Mühendisliği Bölümüen_US
dc.description.abstractEnergy management in power grids becomes essential to reduce the cost for the consumer and improve the power supply reliability. The microgrid is a vital part of the smart grid and it requires intelligent power management approach for effective functioning. Presently, delivering demand load and sustaining energy are two major challenges that exist in the power system. To resolve these problems, short-term load forecasting (STLF) models have been presented as an effective management and energy supply mode in power systems. The recently developed deep learning (DL) and machine learning (ML) models can be employed for accurate STLF in microgrids. In this view, this study presents an intelligent wild geese algorithm with deep learning driven short term load forecasting (IWGADL-STLF) model for sustainable energy management in microgrids. The proposed IWGADL-STLF model intends to accurately and rapidly predict the STLF in the microgrids. To accomplish this, the IWGADL-STLF model uses attention based Bi-directional long short term memory (ABiLSTM) model which involves the input parameters as formation of household and commercial load profiles with commercial load profile of the microgrid as output. The proposed IWGADL-STLF model identifies the behavioural patterns of parameters and models the behaviour in short time period for effective prediction process. Since hyper -parameters play a vital role in the DL models, in this study, WGA is applied as a hyperparameter optimizer of the ABiLSTM model. The IWGADL-STLF approach has shown effective results with low MAE, MAPE, and R2 values. A comprehensive experimental analysis reported the enhanced performance of the presented model over the other existing approaches under several aspects.en_US
dc.identifier.doi10.1016/j.suscom.2022.100813
dc.identifier.issn2210-5379
dc.identifier.issn2210-5387
dc.identifier.scopus2-s2.0-85141283340en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttps://doi.org/10.1016/j.suscom.2022.100813
dc.identifier.urihttps://hdl.handle.net/20.500.12684/12502
dc.identifier.volume36en_US
dc.identifier.wosWOS:000899383500007en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.institutionauthorGürel, Ali Etem
dc.language.isoenen_US
dc.publisherElsevieren_US
dc.relation.ispartofSustainable Computing-Informatics & Systemsen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.snmz$2023V1Guncelleme$en_US
dc.subjectSustainability; Energy Management; Smart Grids; Deep Learning; Hyperparameter Optimization; Short Term Load Forecastingen_US
dc.subjectModelen_US
dc.titleIntelligent wild geese algorithm with deep learning driven short term load forecasting for sustainable energy management in microgridsen_US
dc.typeArticleen_US

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