Long short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting

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dc.authoridekinci, firat/0000-0002-4888-7881
dc.authoridBilgili, Mehmet/0000-0002-5339-6120
dc.authoridYildirim, Alper/0000-0003-2626-1666
dc.authoridCELEBI, Kerimcan/0000-0001-6294-0872
dc.authoridOzbek, Arif/0000-0003-1287-9078
dc.contributor.authorBilgili, Mehmet
dc.contributor.authorYildirim, Alper
dc.contributor.authorOzbek, Arif
dc.contributor.authorCelebi, Kerimcan
dc.contributor.authorEkinci, Firat
dc.date.accessioned2025-01-06T17:37:23Z
dc.date.available2025-01-06T17:37:23Z
dc.date.issued2021
dc.description.abstractRenewable energy sources are developing rapidly worldwide because they are unlimited and permanent, available in every country and also eliminate foreign dependency. In this respect, accurate renewable electricity generation (REG) forecasting is essential in a country's energy planning in relation to its development. In this study, two different data-driven methods such as adaptive neuro-fuzzy inference system (ANFIS) with fuzzy c-means (FCM) and long short-term memory (LSTM) neural network were applied to perform one-day ahead short-term REG forecasting. In addition, short-term hydropower electricity generation (HEG), geothermal electricity generation (GEG), and bioenergy electricity generation (BEG) forecasting were also made using these methods. The correlation coefficient (R), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) were used as evaluation criteria. The values predicted by the ANFIS-FCM and LSTM models were compared with the actual values by evaluating their errors. According to the test results obtained in terms of MAPE evaluation criteria, the best estimation model was obtained for GEG. The lowest MAPE values were found to be 7.20%, 7.46%, 1.63%, and 2.46% for REG, HEG, GEG, and BEG estimates, respectively. The results showed that both ANFIS and LSTM models presented satisfying performances in daily REG prediction, and the ANFIS and LSTM models gave almost identical results.
dc.description.sponsorshipOffice of Scientific Research Projects of Cukurova University [FBA-201911937]
dc.description.sponsorshipThe authors wish to thank the Office of Scientific Research Projects of Cukurova University for funding this project under contract no. FBA-201911937.
dc.identifier.doi10.1080/15435075.2020.1865375
dc.identifier.endpage594
dc.identifier.issn1543-5075
dc.identifier.issn1543-5083
dc.identifier.issue6
dc.identifier.scopus2-s2.0-85098497455
dc.identifier.scopusqualityQ2
dc.identifier.startpage578
dc.identifier.urihttps://doi.org/10.1080/15435075.2020.1865375
dc.identifier.urihttps://hdl.handle.net/20.500.14669/2217
dc.identifier.volume18
dc.identifier.wosWOS:000603821800001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherTaylor & Francis Inc
dc.relation.ispartofInternational Journal of Green Energy
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_20241211
dc.subjectDeep learning
dc.subjectrenewable electricity generation
dc.subjectadaptive neuro-fuzzy inference system (ANFIS)
dc.subjectlong short-term memory (LSTM)
dc.subjectshort-term forecasting
dc.titleLong short-term memory (LSTM) neural network and adaptive neuro-fuzzy inference system (ANFIS) approach in modeling renewable electricity generation forecasting
dc.typeArticle

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