Powerformer: A temporal-based transformer model for wind power forecasting
Wind Power Forecasting has emerged as a critical and dynamic research area in response to the growing demand for renewable energy. The unpredictable and stochastic nature of wind conditions, encompassing factors such as wind speed, wind direction, air temperature, and barometric pressure, poses uniq...
| Main Authors: | , , , , , |
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| Format: | Article |
| Jezik: | English |
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Elsevier
2024-06-01
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| Serija: | Energy Reports |
| Teme: | |
| Online dostop: | http://www.sciencedirect.com/science/article/pii/S2352484723016220 |
| _version_ | 1827400864692699136 |
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| author | Site Mo Haoxin Wang Bixiong Li Zhe Xue Songhai Fan Xianggen Liu |
| author_facet | Site Mo Haoxin Wang Bixiong Li Zhe Xue Songhai Fan Xianggen Liu |
| author_sort | Site Mo |
| collection | DOAJ |
| description | Wind Power Forecasting has emerged as a critical and dynamic research area in response to the growing demand for renewable energy. The unpredictable and stochastic nature of wind conditions, encompassing factors such as wind speed, wind direction, air temperature, and barometric pressure, poses unique challenges for accurate forecasting of wind power generation. Reliable wind power generation forecasts are essential for optimizing energy grid management, ensuring grid stability, and facilitating the integration of wind energy with existing power systems. To address these challenges, this research introduces Powerformer, a Transformer-based model designed to improve the accuracy of wind power prediction. Powerformer utilizes the infrastructure of the Transformer with innovative modifications to address the complexity of wind power prediction, enhancing temporal feature extraction capabilities while reducing complexity. The research in this study includes a comprehensive set of experiments, revealing that Powerformer achieves superior results among all models. Furthermore, the model exhibits stronger robustness, as confirmed through a series of ablation experiments validating the reasonableness of the model design. |
| first_indexed | 2024-03-08T20:09:49Z |
| format | Article |
| id | doaj.art-1f1f6127f90942d58eacd424c5a5f43a |
| institution | Directory Open Access Journal |
| issn | 2352-4847 |
| language | English |
| last_indexed | 2024-03-08T20:09:49Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy Reports |
| spelling | doaj.art-1f1f6127f90942d58eacd424c5a5f43a2023-12-23T05:22:15ZengElsevierEnergy Reports2352-48472024-06-0111736744Powerformer: A temporal-based transformer model for wind power forecastingSite Mo0Haoxin Wang1Bixiong Li2Zhe Xue3Songhai Fan4Xianggen Liu5College of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Electrical Engineering, Sichuan University, Chengdu 610065, ChinaCollege of Architecture and Environment, Sichuan University, Chengdu 610065, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, ChinaState Grid Sichuan Electric Power Research Institute, Chengdu 610041, ChinaCollege of Computer Science, Sichuan University, Chengdu 610065, China; Corresponding author.Wind Power Forecasting has emerged as a critical and dynamic research area in response to the growing demand for renewable energy. The unpredictable and stochastic nature of wind conditions, encompassing factors such as wind speed, wind direction, air temperature, and barometric pressure, poses unique challenges for accurate forecasting of wind power generation. Reliable wind power generation forecasts are essential for optimizing energy grid management, ensuring grid stability, and facilitating the integration of wind energy with existing power systems. To address these challenges, this research introduces Powerformer, a Transformer-based model designed to improve the accuracy of wind power prediction. Powerformer utilizes the infrastructure of the Transformer with innovative modifications to address the complexity of wind power prediction, enhancing temporal feature extraction capabilities while reducing complexity. The research in this study includes a comprehensive set of experiments, revealing that Powerformer achieves superior results among all models. Furthermore, the model exhibits stronger robustness, as confirmed through a series of ablation experiments validating the reasonableness of the model design.http://www.sciencedirect.com/science/article/pii/S2352484723016220Renewable energyWind power forecastingTransformer |
| spellingShingle | Site Mo Haoxin Wang Bixiong Li Zhe Xue Songhai Fan Xianggen Liu Powerformer: A temporal-based transformer model for wind power forecasting Energy Reports Renewable energy Wind power forecasting Transformer |
| title | Powerformer: A temporal-based transformer model for wind power forecasting |
| title_full | Powerformer: A temporal-based transformer model for wind power forecasting |
| title_fullStr | Powerformer: A temporal-based transformer model for wind power forecasting |
| title_full_unstemmed | Powerformer: A temporal-based transformer model for wind power forecasting |
| title_short | Powerformer: A temporal-based transformer model for wind power forecasting |
| title_sort | powerformer a temporal based transformer model for wind power forecasting |
| topic | Renewable energy Wind power forecasting Transformer |
| url | http://www.sciencedirect.com/science/article/pii/S2352484723016220 |
| work_keys_str_mv | AT sitemo powerformeratemporalbasedtransformermodelforwindpowerforecasting AT haoxinwang powerformeratemporalbasedtransformermodelforwindpowerforecasting AT bixiongli powerformeratemporalbasedtransformermodelforwindpowerforecasting AT zhexue powerformeratemporalbasedtransformermodelforwindpowerforecasting AT songhaifan powerformeratemporalbasedtransformermodelforwindpowerforecasting AT xianggenliu powerformeratemporalbasedtransformermodelforwindpowerforecasting |