Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction
The intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current...
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Elsevier
2025-09-01
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| Series: | International Journal of Electrical Power & Energy Systems |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S0142061525004934 |
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| author | Ceren Ceylan Zehra Yumurtacı |
| author_facet | Ceren Ceylan Zehra Yumurtacı |
| author_sort | Ceren Ceylan |
| collection | DOAJ |
| description | The intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current literature applies single-algorithm based on each individual energy source and less multi-algorithm based on comparative studies on multiple architectures as applied to integrated hybrid systems. In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). Using five years of Istanbul meteorological data (2018–2022), optimal algorithms were systematically identified for each parameter through rigorous hyperparameter optimization and cross-validation. Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 °C, R2: 0.9976). The integrated approach predicted annual hybrid system energy production with 89 % accuracy, demonstrating 0.48 % deviation from observed values. Most significantly, our framework successfully forecasted sixth year (2023) energy production with 1.55 % error, validating its real-world applicability. This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures. |
| format | Article |
| id | doaj-art-5f9b656a9571478dbee6cd4b2623a2fe |
| institution | DOAJ |
| issn | 0142-0615 |
| language | English |
| publishDate | 2025-09-01 |
| publisher | Elsevier |
| record_format | Article |
| series | International Journal of Electrical Power & Energy Systems |
| spelling | doaj-art-5f9b656a9571478dbee6cd4b2623a2fe2025-08-20T03:03:46ZengElsevierInternational Journal of Electrical Power & Energy Systems0142-06152025-09-0117011094510.1016/j.ijepes.2025.110945Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter predictionCeren Ceylan0Zehra Yumurtacı1Mechanical Engineering-Energy, Yildiz Technical University, Istanbul, Turkey; Energy System Engineering Department, Atilim University, Ankara, Turkey; Corresponding author at: Mechanical Engineering-Energy, Yildiz Technical University, Istanbul, Turkey.Mechanical Engineering-Energy, Yildiz Technical University, Istanbul, TurkeyThe intermittent nature of renewable energy sources necessitates accurate power production forecasting to ensure system sustainability and balance between energy supply and demand. Although the deep learning-based meteorological forecasting is significantly studied in literature, most of the current literature applies single-algorithm based on each individual energy source and less multi-algorithm based on comparative studies on multiple architectures as applied to integrated hybrid systems. In addition, most of the research uses the same algorithmic solution to all the meteorological parameters without identifying parameter-specific optimization potential, and recent research is verified on actual future time steps instead of historical train-test split. This study presents a comprehensive comparative analysis of five deep learning algorithms, Multilayer Perceptron (MLP), Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and CNN-LSTM hybrid, for forecasting critical meteorological parameters (wind speed, ambient temperature, and solar radiation) that determine energy output in a wind and solar-based hybrid energy system (HES). Using five years of Istanbul meteorological data (2018–2022), optimal algorithms were systematically identified for each parameter through rigorous hyperparameter optimization and cross-validation. Key results demonstrate that GRU achieves superior performance in wind speed prediction (RMSE: 0.049 m/s, R2: 0.8634) and solar radiation forecasting (RMSE: 0.146 W/m2, R2: 0.6643), while CNN-LSTM excels in ambient temperature prediction (RMSE: 0.011 °C, R2: 0.9976). The integrated approach predicted annual hybrid system energy production with 89 % accuracy, demonstrating 0.48 % deviation from observed values. Most significantly, our framework successfully forecasted sixth year (2023) energy production with 1.55 % error, validating its real-world applicability. This research contributes to the methodological advancement of renewable energy forecasting by systematically identifying optimal algorithmic approaches for different meteorological parameters in hybrid systems, thereby supporting the integration of intermittent renewable sources into sustainable energy infrastructures.http://www.sciencedirect.com/science/article/pii/S0142061525004934GRUCNN-LSTMRenewable energy predictionHybrid energy systemDeep learning |
| spellingShingle | Ceren Ceylan Zehra Yumurtacı Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction International Journal of Electrical Power & Energy Systems GRU CNN-LSTM Renewable energy prediction Hybrid energy system Deep learning |
| title | Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| title_full | Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| title_fullStr | Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| title_full_unstemmed | Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| title_short | Precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| title_sort | precision forecasting for hybrid energy systems using five deep learning algorithms for meteorological parameter prediction |
| topic | GRU CNN-LSTM Renewable energy prediction Hybrid energy system Deep learning |
| url | http://www.sciencedirect.com/science/article/pii/S0142061525004934 |
| work_keys_str_mv | AT cerenceylan precisionforecastingforhybridenergysystemsusingfivedeeplearningalgorithmsformeteorologicalparameterprediction AT zehrayumurtacı precisionforecastingforhybridenergysystemsusingfivedeeplearningalgorithmsformeteorologicalparameterprediction |