Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data
Forecasting 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 appl...
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IEEE
2025-01-01
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| Online Access: | https://ieeexplore.ieee.org/document/11015433/ |
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| author | Necati Aksoy Alper Yilmaz Gokay Bayrak Mehmet Koc |
| author_facet | Necati Aksoy Alper Yilmaz Gokay Bayrak Mehmet Koc |
| author_sort | Necati Aksoy |
| collection | DOAJ |
| description | Forecasting 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. |
| format | Article |
| id | doaj-art-5e3f941db6c5431598cc00fbc9552ea8 |
| institution | OA Journals |
| issn | 2169-3536 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj-art-5e3f941db6c5431598cc00fbc9552ea82025-08-20T02:19:31ZengIEEEIEEE Access2169-35362025-01-0113932879330110.1109/ACCESS.2025.357344311015433Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World DataNecati Aksoy0https://orcid.org/0000-0003-1496-2916Alper Yilmaz1https://orcid.org/0000-0003-3736-3668Gokay Bayrak2https://orcid.org/0000-0002-5136-0829Mehmet Koc3https://orcid.org/0000-0001-9665-6650Department of Electrical and Electronics Engineering, Renewable Energy Systems and Smart Grids Laboratory, Bursa Technical University, Bursa, TürkiyeDepartment of Electrical and Electronics Engineering, Renewable Energy Systems and Smart Grids Laboratory, Bursa Technical University, Bursa, TürkiyeDepartment of Electrical and Electronics Engineering, Renewable Energy Systems and Smart Grids Laboratory, Bursa Technical University, Bursa, TürkiyeUludağ Elektrik Dağıtım A.Ş. (UEDAŞ), Bursa, TürkiyeForecasting 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.https://ieeexplore.ieee.org/document/11015433/Deep learningNeuralProphetpredictive modelsrenewable energysolar power forecasting |
| spellingShingle | Necati Aksoy Alper Yilmaz Gokay Bayrak Mehmet Koc Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data IEEE Access Deep learning NeuralProphet predictive models renewable energy solar power forecasting |
| title | Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data |
| title_full | Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data |
| title_fullStr | Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data |
| title_full_unstemmed | Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data |
| title_short | Eliminating Meteorological Dependencies in Solar Power Forecasting: A Deep Learning Solution With NeuralProphet and Real-World Data |
| title_sort | eliminating meteorological dependencies in solar power forecasting a deep learning solution with neuralprophet and real world data |
| topic | Deep learning NeuralProphet predictive models renewable energy solar power forecasting |
| url | https://ieeexplore.ieee.org/document/11015433/ |
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