Prediction of hydroelectric power generation with machine learning and innovative combined deep learning techniques

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dc.contributor.authorYilmaz, Banu
dc.contributor.authorAras, Egemen
dc.contributor.authorSamadianfard, Saeed
dc.date.accessioned2026-02-08T15:14:55Z
dc.date.available2026-02-08T15:14:55Z
dc.date.issued2026
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractDams provide energy production by the accumulation and storage of water. Therefore, changes in weather conditions directly affect production capacity and energy efficiency. While the amount of precipitation determines the circulation capacity of water resources, temperature affects the evaporation rate of water and thus water levels. Flow is one of the critical parameters required to determine the amount of water needed for energy production and to ensure efficient energy production. Within the scope of this study, energy production forecasting models have been established for the Alt & imath;nkaya Dam Basin, which has significant potential for hydroelectric energy production in Turkey. In addition to long-short-term memory (LSTM) and feed-forward neural network (FFNN) methods, TPAFFNN-LSTM, which combines these methods with an innovative temporal pattern attention (TPA) mechanism, was also used. Random forest (RF) and extreme gradient boosting (XGB) are also used to evaluate the efficiency and accuracy of the proposed models. As a feature selection method, LASSO regression was applied before the analyses. Shapley Additive Explanations (SHAP) and Regression Receiver Operating Characteristic (RROC) analyses were used in the evaluation phase of all models. According to the results obtained, the nRMSE and NSE criteria of the TPAFFNN-LSTM method were obtained as 0.16 and 0.69, respectively. These results were found to be 18% and 19% more successful than the other methods. The proposed method represents a significant advancement in hydropower energy generation forecasting, providing a robust framework that combines depth of analysis with clarity of insights.
dc.identifier.doi10.1007/s00477-025-03140-8
dc.identifier.issn1436-3240
dc.identifier.issn1436-3259
dc.identifier.issue1
dc.identifier.scopus2-s2.0-105026338280
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.1007/s00477-025-03140-8
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5500
dc.identifier.volume40
dc.identifier.wosWOS:001654834400005
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSpringer
dc.relation.ispartofStochastic Environmental Research and Risk Assessment
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzWOS_KA_20260207
dc.subjectHydroelectric power generation
dc.subjectMachine learning
dc.subjectAlt & imath;nkaya dam
dc.subjectInnovative deep learning
dc.titlePrediction of hydroelectric power generation with machine learning and innovative combined deep learning techniques
dc.typeArticle

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