A novel hybrid model for short-term wind power forecasting
Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance...
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| Published in | Applied soft computing Vol. 80; pp. 93 - 106 |
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| Main Authors | , , , |
| Format | Journal Article |
| Language | English |
| Published |
Elsevier B.V
01.07.2019
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| Subjects | |
| Online Access | Get full text |
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| Abstract | Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models.
•Propose a novel hybrid forecasting model based on multi-objective optimization.•The proposed model is superior to 18 comparison models for wind power prediction.•The proposed hybrid model demonstrates higher prediction accuracy and reliability.•Hypothesis testing is used to make a comprehensive evaluation for proposed model. |
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| AbstractList | Wind energy prediction has a significant effect on the planning, economic operation and security maintenance of the wind power system. However, due to the high volatility and intermittency, it is difficult to model and predict wind power series through traditional forecasting approaches. To enhance prediction accuracy, this study developed a hybrid model that incorporates the following stages. First, an improved complete ensemble empirical mode decomposition with adaptive noise technology was applied to decompose the wind energy series for eliminating noise and extracting the main features of original data. Next, to achieve high accurate and stable forecasts, an improved wavelet neural network optimized by optimization methods was built and used to implement wind energy prediction. Finally, hypothesis testing, stability test and four case studies including eighteen comparison models were utilized to test the abilities of prediction models. The experimental results show that the average values of the mean absolute percent errors of the proposed hybrid model are 5.0116% (one-step ahead), 7.7877% (two-step ahead) and 10.6968% (three-step ahead), which are much lower than comparison models.
•Propose a novel hybrid forecasting model based on multi-objective optimization.•The proposed model is superior to 18 comparison models for wind power prediction.•The proposed hybrid model demonstrates higher prediction accuracy and reliability.•Hypothesis testing is used to make a comprehensive evaluation for proposed model. |
| Author | Wang, Jianzhou Yang, Wendong Niu, Tong Du, Pei |
| Author_xml | – sequence: 1 givenname: Pei surname: Du fullname: Du, Pei – sequence: 2 givenname: Jianzhou surname: Wang fullname: Wang, Jianzhou email: wjzdufe@126.com – sequence: 3 givenname: Wendong surname: Yang fullname: Yang, Wendong – sequence: 4 givenname: Tong surname: Niu fullname: Niu, Tong |
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| SubjectTerms | Hybrid forecasting model Multi-objective Optimization Algorithm Wavelet neural network Wind power forecasting |
| Title | A novel hybrid model for short-term wind power forecasting |
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