Wind power forecasting based on stacking ensemble model, decomposition and intelligent optimization algorithm
Wind power forecasting has high application value in power systems. However, due to the intermittence and fluctuation of wind power, it is difficult to predict wind power effectively using a single forecasting model. Therefore, to improve the accuracy and stability of wind power forecasting, an ense...
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| Vydáno v: | Neurocomputing (Amsterdam) Ročník 462; s. 169 - 184 |
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| Hlavní autoři: | , , , |
| Médium: | Journal Article |
| Jazyk: | angličtina |
| Vydáno: |
Elsevier B.V
28.10.2021
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| Témata: | |
| ISSN: | 0925-2312 |
| On-line přístup: | Získat plný text |
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| Abstract | Wind power forecasting has high application value in power systems. However, due to the intermittence and fluctuation of wind power, it is difficult to predict wind power effectively using a single forecasting model. Therefore, to improve the accuracy and stability of wind power forecasting, an ensemble learning model based on stacking framework is proposed in this paper. First, several decomposition techniques are used to pre-process the original wind power data and an optimal decomposition method is selected through experiments. Then, a quadratic interpolation based on state transition algorithm is proposed to optimize the parameters of the Bernstein polynomial model and the weights of the Hermite neural network (HNN) to obtain two base learners. Finally, the Spearman correlation coefficient is used to analyze the correlation of several base learners. The base learners with low correlation and strong prediction ability are selected as the first-layer forecasting model of the stacking model, and the HNN is used as the second-layer prediction model to obtain the stacking ensemble model. To verify the effectiveness of the proposed model, a large number of comprehensive experiments are carried out with wind power data from a wind farm in Xinjiang, China. Experimental results show that the proposed model has higher prediction accuracy and stability than other single forecasting models. |
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| AbstractList | Wind power forecasting has high application value in power systems. However, due to the intermittence and fluctuation of wind power, it is difficult to predict wind power effectively using a single forecasting model. Therefore, to improve the accuracy and stability of wind power forecasting, an ensemble learning model based on stacking framework is proposed in this paper. First, several decomposition techniques are used to pre-process the original wind power data and an optimal decomposition method is selected through experiments. Then, a quadratic interpolation based on state transition algorithm is proposed to optimize the parameters of the Bernstein polynomial model and the weights of the Hermite neural network (HNN) to obtain two base learners. Finally, the Spearman correlation coefficient is used to analyze the correlation of several base learners. The base learners with low correlation and strong prediction ability are selected as the first-layer forecasting model of the stacking model, and the HNN is used as the second-layer prediction model to obtain the stacking ensemble model. To verify the effectiveness of the proposed model, a large number of comprehensive experiments are carried out with wind power data from a wind farm in Xinjiang, China. Experimental results show that the proposed model has higher prediction accuracy and stability than other single forecasting models. |
| Author | Dong, Yingchao Wang, Cong Zhou, Xiaojun Zhang, Hongli |
| Author_xml | – sequence: 1 givenname: Yingchao surname: Dong fullname: Dong, Yingchao organization: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China – sequence: 2 givenname: Hongli surname: Zhang fullname: Zhang, Hongli email: zhlxju@163.com organization: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China – sequence: 3 givenname: Cong surname: Wang fullname: Wang, Cong organization: School of Electrical Engineering, Xinjiang University, Urumqi, Xinjiang 830047, China – sequence: 4 givenname: Xiaojun surname: Zhou fullname: Zhou, Xiaojun organization: School of Automation, Central South University, Changsha 410083, China |
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