Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement
Abstract Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study pres...
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| Language: | English |
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Nature Portfolio
2025-01-01
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| Series: | Scientific Reports |
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| Online Access: | https://doi.org/10.1038/s41598-024-80424-z |
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| author | Dácil Díaz-Bello Carlos Vargas-Salgado Manuel Alcazar-Ortega David Alfonso-Solar |
| author_facet | Dácil Díaz-Bello Carlos Vargas-Salgado Manuel Alcazar-Ortega David Alfonso-Solar |
| author_sort | Dácil Díaz-Bello |
| collection | DOAJ |
| description | Abstract Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction. This methodology dynamically adjusts the neural network parameters during training, including the number of neurons, transfer functions, weights, and biases, to minimize the root mean square error. Evaluation was performed on twelve representative days using annual, monthly, and seasonal data, and a comparison was made with multiple linear regression and nonlinear autoregressive neural network models, demonstrating the approach’s effectiveness. Evaluation metrics such as mean square error, R-value, and mean percentage error reveal promising prediction accuracy. MATLAB is used for modeling, training, and testing, and a real 4.2 kW PV plant is used for validation. The results indicate significant improvements, with mean square errors as low as 20 W on cloudy days and 175 W on sunny days. The proposed methodology achieves prediction versus target regressions consistency, with R values ranging from 0.95824 to 0.99980, highlighting its efficiency in providing reliable predictions of PV power generation. |
| format | Article |
| id | doaj-art-e59a31d0ad3e4393b818890de74e88b2 |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-e59a31d0ad3e4393b818890de74e88b22025-02-02T12:15:45ZengNature PortfolioScientific Reports2045-23222025-01-0115112910.1038/s41598-024-80424-zOptimizing photovoltaic power plant forecasting with dynamic neural network structure refinementDácil Díaz-Bello0Carlos Vargas-Salgado1Manuel Alcazar-Ortega2David Alfonso-Solar3Instituto de Ingeniería Energética, Universitat Politècnica de ValènciaInstituto de Ingeniería Energética, Universitat Politècnica de ValènciaInstituto de Ingeniería Energética, Universitat Politècnica de ValènciaInstituto de Ingeniería Energética, Universitat Politècnica de ValènciaAbstract Reliable prediction of photovoltaic power generation is key to the efficient management of energy systems in response to the inherent uncertainty of renewable energy sources. Despite advances in weather forecasting, photovoltaic power prediction accuracy remains a challenge. This study presents a novel approach that combines genetic algorithms and dynamic neural network structure refinement to optimize photovoltaic prediction. This methodology dynamically adjusts the neural network parameters during training, including the number of neurons, transfer functions, weights, and biases, to minimize the root mean square error. Evaluation was performed on twelve representative days using annual, monthly, and seasonal data, and a comparison was made with multiple linear regression and nonlinear autoregressive neural network models, demonstrating the approach’s effectiveness. Evaluation metrics such as mean square error, R-value, and mean percentage error reveal promising prediction accuracy. MATLAB is used for modeling, training, and testing, and a real 4.2 kW PV plant is used for validation. The results indicate significant improvements, with mean square errors as low as 20 W on cloudy days and 175 W on sunny days. The proposed methodology achieves prediction versus target regressions consistency, with R values ranging from 0.95824 to 0.99980, highlighting its efficiency in providing reliable predictions of PV power generation.https://doi.org/10.1038/s41598-024-80424-zGenetic algorithmsArtificial neural networksForecastingPhotovoltaic energyOptimizationModeling |
| spellingShingle | Dácil Díaz-Bello Carlos Vargas-Salgado Manuel Alcazar-Ortega David Alfonso-Solar Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement Scientific Reports Genetic algorithms Artificial neural networks Forecasting Photovoltaic energy Optimization Modeling |
| title | Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| title_full | Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| title_fullStr | Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| title_full_unstemmed | Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| title_short | Optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| title_sort | optimizing photovoltaic power plant forecasting with dynamic neural network structure refinement |
| topic | Genetic algorithms Artificial neural networks Forecasting Photovoltaic energy Optimization Modeling |
| url | https://doi.org/10.1038/s41598-024-80424-z |
| work_keys_str_mv | AT dacildiazbello optimizingphotovoltaicpowerplantforecastingwithdynamicneuralnetworkstructurerefinement AT carlosvargassalgado optimizingphotovoltaicpowerplantforecastingwithdynamicneuralnetworkstructurerefinement AT manuelalcazarortega optimizingphotovoltaicpowerplantforecastingwithdynamicneuralnetworkstructurerefinement AT davidalfonsosolar optimizingphotovoltaicpowerplantforecastingwithdynamicneuralnetworkstructurerefinement |