A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting
In the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy sources, traditional data-driven deep learning methods face challenges in capturing long-term depende...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2024-12-01
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| Series: | Frontiers in Energy Research |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1461410/full |
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| author | Junchi He Junchi He Tian Tian Yaqing Wu Xiaolu Liu Mengli Wei |
| author_facet | Junchi He Junchi He Tian Tian Yaqing Wu Xiaolu Liu Mengli Wei |
| author_sort | Junchi He |
| collection | DOAJ |
| description | In the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy sources, traditional data-driven deep learning methods face challenges in capturing long-term dependencies. This paper proposes a hybrid model that combines Sparse Identification (SI) with Convolutional Neural Networks (CNN) to enhance the interpretability and generalization of predictions. The SI method is utilized to extract trends, seasonality, and periodicity, while the deep neural network captures complex relationships. Experimental results demonstrate that the model exhibits high accuracy and practicality in forecasting new energy scenario data, contributing to the advancement of time series prediction methodologies. |
| format | Article |
| id | doaj-art-bf658a6574e8495983069baaeaaf2ea5 |
| institution | DOAJ |
| issn | 2296-598X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Energy Research |
| spelling | doaj-art-bf658a6574e8495983069baaeaaf2ea52025-08-20T02:52:19ZengFrontiers Media S.A.Frontiers in Energy Research2296-598X2024-12-011210.3389/fenrg.2024.14614101461410A hybrid sparse identification and convolutional neural network framework for renewable energy forecastingJunchi He0Junchi He1Tian Tian2Yaqing Wu3Xiaolu Liu4Mengli Wei5School of Electrical Engineering, China University of Mining and Technology, Xuzhou, ChinaState Grid Lianyungang Power Supply Company, Lianyungang, ChinaState Grid Lianyungang Power Supply Company, Lianyungang, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaSchool of Automation, Nanjing Institute of Technology, Nanjing, ChinaSchool of Cyber Science and Engineering, Southeast University, Nanjing, ChinaIn the field of renewable energy, accurate long-term time series forecasting is crucial for optimizing the operation of power systems and reducing risks. Due to the intermittency of renewable energy sources, traditional data-driven deep learning methods face challenges in capturing long-term dependencies. This paper proposes a hybrid model that combines Sparse Identification (SI) with Convolutional Neural Networks (CNN) to enhance the interpretability and generalization of predictions. The SI method is utilized to extract trends, seasonality, and periodicity, while the deep neural network captures complex relationships. Experimental results demonstrate that the model exhibits high accuracy and practicality in forecasting new energy scenario data, contributing to the advancement of time series prediction methodologies.https://www.frontiersin.org/articles/10.3389/fenrg.2024.1461410/fulltime series forecastinglong-term predictionsparse identificationconvolutional neural networksrenewable energy |
| spellingShingle | Junchi He Junchi He Tian Tian Yaqing Wu Xiaolu Liu Mengli Wei A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting Frontiers in Energy Research time series forecasting long-term prediction sparse identification convolutional neural networks renewable energy |
| title | A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| title_full | A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| title_fullStr | A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| title_full_unstemmed | A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| title_short | A hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| title_sort | hybrid sparse identification and convolutional neural network framework for renewable energy forecasting |
| topic | time series forecasting long-term prediction sparse identification convolutional neural networks renewable energy |
| url | https://www.frontiersin.org/articles/10.3389/fenrg.2024.1461410/full |
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