Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting
As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict bec...
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MDPI AG
2025-07-01
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| Series: | Sensors |
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| Online Access: | https://www.mdpi.com/1424-8220/25/14/4489 |
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| author | Nakhun Song Roberto Chang-Silva Kyungil Lee Seonyoung Park |
| author_facet | Nakhun Song Roberto Chang-Silva Kyungil Lee Seonyoung Park |
| author_sort | Nakhun Song |
| collection | DOAJ |
| description | As global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems. |
| format | Article |
| id | doaj-art-0931e348041e42e988a0d0691e2a5897 |
| institution | DOAJ |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj-art-0931e348041e42e988a0d0691e2a58972025-08-20T02:47:17ZengMDPI AGSensors1424-82202025-07-012514448910.3390/s25144489Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power ForecastingNakhun Song0Roberto Chang-Silva1Kyungil Lee2Seonyoung Park3Department of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaDepartment of Applied Artificial Intelligence, Seoul National University of Science and Technology, 232 Gongneung-ro, Nowon-gu, Seoul 01811, Republic of KoreaAs global electricity demand increases and concerns over fossil fuel usage intensify, renewable energy sources have gained significant attention. Solar energy stands out due to its low installation costs and suitability for deployment. However, solar power generation remains difficult to predict because of its dependence on weather conditions and decentralized infrastructure. To address this challenge, this study proposes a flexible hybrid ensemble (FHE) framework that dynamically selects the most appropriate base model based on prediction error patterns. Unlike traditional ensemble methods that aggregate all base model outputs, the FHE employs a meta-model to leverage the strengths of individual models while mitigating their weaknesses. The FHE is evaluated using data from four solar power plants and is benchmarked against several state-of-the-art models and conventional hybrid ensemble techniques. Experimental results demonstrate that the FHE framework achieves superior predictive performance, improving the Mean Absolute Percentage Error by 30% compared to the SVR model. Moreover, the FHE model maintains high accuracy across diverse weather conditions and eliminates the need for preliminary validation of base and ensemble models, streamlining the deployment process. These findings highlight the FHE framework’s potential as a robust and scalable solution for forecasting in small-scale distributed solar power systems.https://www.mdpi.com/1424-8220/25/14/4489solar forecastinghybrid ensemblemeta-learningmeta-modelingsolar energy systemsrenewable integration |
| spellingShingle | Nakhun Song Roberto Chang-Silva Kyungil Lee Seonyoung Park Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting Sensors solar forecasting hybrid ensemble meta-learning meta-modeling solar energy systems renewable integration |
| title | Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting |
| title_full | Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting |
| title_fullStr | Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting |
| title_full_unstemmed | Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting |
| title_short | Dynamic Model Selection in a Hybrid Ensemble Framework for Robust Photovoltaic Power Forecasting |
| title_sort | dynamic model selection in a hybrid ensemble framework for robust photovoltaic power forecasting |
| topic | solar forecasting hybrid ensemble meta-learning meta-modeling solar energy systems renewable integration |
| url | https://www.mdpi.com/1424-8220/25/14/4489 |
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