Deep learning approaches for robust prediction of large-scale renewable energy generation: A comprehensive comparative study from a national context

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dc.contributor.authorAksoy, Necati
dc.contributor.authorGenc, Istemihan
dc.date.accessioned2026-02-08T15:15:45Z
dc.date.available2026-02-08T15:15:45Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractPrecise forecasting of renewable energy generation is crucial for ensuring grid stability and enhancing the efficiency of energy management systems. This research develops and rigorously evaluates a range of deep learning models-such as Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM) networks, Gated Recurrent Units (GRUs), and Bidirectional LSTM (BiLSTM) architectures-for predicting solar, wind, and total renewable energy production at a national scale. These models are systematically benchmarked against traditional machine learning approaches and gradient boosting methods to determine their predictive capabilities. The findings demonstrate that deep learning models incorporating memory mechanisms consistently surpass conventional methods, with BiLSTM standing out as the most precise and dependable model. Furthermore, the study investigates fully connected artificial neural networks (ANNs) and ConvLSTM2D models, reinforcing the advantages of memory-based architectures in modeling temporal relationships. By introducing a robust deep learning framework for large-scale renewable energy forecasting, this research represents a considerable leap forward compared to traditional machine learning techniques. The results highlight the transformative potential of deep learning in improving forecasting accuracy, thereby facilitating more effective energy planning and the smooth integration of renewable energy into national power grids.
dc.identifier.doi10.1177/1088467X251325068
dc.identifier.issn1088-467X
dc.identifier.issn1571-4128
dc.identifier.scopus2-s2.0-105021885332
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.1177/1088467X251325068
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5949
dc.identifier.wosWOS:001482283300001
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherSage Publications Inc
dc.relation.ispartofIntelligent Data Analysis
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectDeep learning
dc.subjectrenewable energy
dc.subjectBiLSTM
dc.subjectpredictive models
dc.subjectartificial intelligence
dc.titleDeep learning approaches for robust prediction of large-scale renewable energy generation: A comprehensive comparative study from a national context
dc.typeArticle

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