Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data

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dc.authorid0000-0001-9665-6650
dc.authorid0000-0003-3736-3668
dc.authorid0000-0002-5136-0829
dc.contributor.authorAksoy, Necati
dc.contributor.authorYilmaz, Alper
dc.contributor.authorBayrak, Gokay
dc.contributor.authorKoc, Mehmet
dc.date.accessioned2026-02-08T15:15:41Z
dc.date.available2026-02-08T15:15:41Z
dc.date.issued2025
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractForecasting solar power generation is essential for efficient energy management and grid stability. However, existing predictive models often rely on external datasets, such as meteorological and sensor data, to make accurate predictions. This dependency introduces complexities and limits their application in data-sparse scenarios. In this study, we propose a novel forecasting approach based on the NeuralProphet algorithm, a deep learning model that predicts solar power generation solely from its historical data, eliminating reliance on additional input data. To evaluate the proposed approach, we conducted two case studies. The first utilized a 10-month dataset from a 1.2 kW small-scale solar power unit at Bursa Technical University's Smart Grids laboratory, recorded at 15-minute intervals. Despite the limited dataset, the model achieved an R-squared value exceeding 0.74, demonstrating promising predictive capability. The second case study applied the NeuralProphet-based model to a large-scale dataset of nationwide solar power generation in Germany, spanning five years and collected at 15-minute intervals. Models trained on this dataset achieved R-squared values exceeding 0.99, highlighting the algorithm's capacity to effectively capture seasonal and temporal patterns at a national scale. Our results indicate that the NeuralProphet-based forecasting approach offers a viable and efficient alternative for solar power prediction, achieving high accuracy without external data dependencies.
dc.identifier.doi10.1109/ACCESS.2025.3573443
dc.identifier.endpage93301
dc.identifier.issn2169-3536
dc.identifier.scopus2-s2.0-105006851345
dc.identifier.scopusqualityQ1
dc.identifier.startpage93287
dc.identifier.urihttps://doi.org/10.1109/ACCESS.2025.3573443
dc.identifier.urihttps://hdl.handle.net/20.500.12885/5898
dc.identifier.volume13
dc.identifier.wosWOS:001502494300017
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Access
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzWOS_KA_20260207
dc.subjectForecasting
dc.subjectPredictive models
dc.subjectSolar power generation
dc.subjectAccuracy
dc.subjectPrediction algorithms
dc.subjectLong short term memory
dc.subjectDeep learning
dc.subjectTraining
dc.subjectTemperature distribution
dc.subjectRenewable energy sources
dc.subjectNeuralProphet
dc.subjectpredictive models
dc.subjectrenewable energy
dc.subjectsolar power forecasting
dc.titleEliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data
dc.typeArticle

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