A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting
Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be...
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| Format: | Article |
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MDPI AG
2022-05-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/10/11/1824 |
| _version_ | 1797492661033107456 |
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| author | Jing Wan Jiehui Huang Zhiyuan Liao Chunquan Li Peter X. Liu |
| author_facet | Jing Wan Jiehui Huang Zhiyuan Liao Chunquan Li Peter X. Liu |
| author_sort | Jing Wan |
| collection | DOAJ |
| description | Short-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness. |
| first_indexed | 2024-03-10T01:06:54Z |
| format | Article |
| id | doaj.art-e70a4735b7804bcf9b5cacea7146ee13 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-10T01:06:54Z |
| publishDate | 2022-05-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-e70a4735b7804bcf9b5cacea7146ee132023-11-23T14:25:10ZengMDPI AGMathematics2227-73902022-05-011011182410.3390/math10111824A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power ForecastingJing Wan0Jiehui Huang1Zhiyuan Liao2Chunquan Li3Peter X. Liu4The School of Qianhu, Nanchang University, Nanchang 330031, ChinaThe School of Information Engineering, Nanchang University, Nanchang 330031, ChinaThe School of Information Engineering, Nanchang University, Nanchang 330031, ChinaThe School of Information Engineering, Nanchang University, Nanchang 330031, ChinaThe School of Information Engineering, Nanchang University, Nanchang 330031, ChinaShort-term wind power forecasting (SWPF) is essential for managing wind power systems management. However, most existing forecasting methods fail to fully consider how to rationally integrate multi-view learning technologies with attention mechanisms. In this case, some potential features cannot be fully extracted, degenerating the predictive accuracy and robustness in SWPF. To solve this problem, this paper proposes a multi-view ensemble width-depth neural network (MVEW-DNN) for SWPF. Specifically, MVEW-DNN consists of local and global view learning subnetworks, which can effectively achieve more potential global and local view features of the original wind power data. In MVEW-DNN, the local view learning subnetwork is developed by introducing the deep belief network (DBN) model, which can efficiently extract the local view features. On the other hand, by introducing the attention mechanism, a new deep encoder board learning system (deBLS) is developed as the global view learning subnetwork, which provides more comprehensive global information. Therefore, by rationally learning the effective local and global view features, MVEW-DNN can achieve competitive predictive performance in SWPF. MVEW-DNN is compared with the state-of-the-art models in SWPF. The experiment results indicate that MVEW-DNN can provide competitive predictive accuracy and robustness.https://www.mdpi.com/2227-7390/10/11/1824renewable energywind power forecastinghybrid modelmachine learning |
| spellingShingle | Jing Wan Jiehui Huang Zhiyuan Liao Chunquan Li Peter X. Liu A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting Mathematics renewable energy wind power forecasting hybrid model machine learning |
| title | A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting |
| title_full | A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting |
| title_fullStr | A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting |
| title_full_unstemmed | A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting |
| title_short | A Multi-View Ensemble Width-Depth Neural Network for Short-Term Wind Power Forecasting |
| title_sort | multi view ensemble width depth neural network for short term wind power forecasting |
| topic | renewable energy wind power forecasting hybrid model machine learning |
| url | https://www.mdpi.com/2227-7390/10/11/1824 |
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