A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting
•A multi-objective optimization algorithm is successfully developed.•Employ a data denoising strategy to process the raw wind speed sequence.•Propose a novel method of determining weight to combine all of individual models.•Design four experiments based on actual wind farms to examine the effectiven...
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| Published in: | Applied energy Vol. 241; pp. 519 - 539 |
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| Main Authors: | , |
| Format: | Journal Article |
| Language: | English |
| Published: |
Elsevier Ltd
01.05.2019
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| Subjects: | |
| ISSN: | 0306-2619, 1872-9118 |
| Online Access: | Get full text |
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| Abstract | •A multi-objective optimization algorithm is successfully developed.•Employ a data denoising strategy to process the raw wind speed sequence.•Propose a novel method of determining weight to combine all of individual models.•Design four experiments based on actual wind farms to examine the effectiveness.
Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise—a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory—and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models. |
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| AbstractList | •A multi-objective optimization algorithm is successfully developed.•Employ a data denoising strategy to process the raw wind speed sequence.•Propose a novel method of determining weight to combine all of individual models.•Design four experiments based on actual wind farms to examine the effectiveness.
Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise—a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory—and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models. Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations. In order to improve the accuracy of wind speed forecasting, a large number of prediction methods have been proposed. However, existing prediction models ignore the role of data preprocessing and are susceptible to various limitations of the single individual model that can lead to low prediction accuracy. In this study, a developed combined model is proposed, including complete ensemble empirical mode decomposition with adaptive noise—a multi-objective grasshopper optimization algorithm based on a no-negative constraint theory—and several single models, including four neural network models and a linear model, to achieve accurate prediction results. The novel combined model considers the linear and nonlinear characteristics of the sequence, successfully overcomes the limitations of the single model, and obtains accurate and stable prediction results. In order to test the performance of combined model, the wind speed sequence of a wind farm from China is used for experiments and discussions. The results of the experiments and discussions show that the novel combined model has better forecasting performance than traditional prediction models. |
| Author | Niu, Xinsong Wang, Jiyang |
| Author_xml | – sequence: 1 givenname: Xinsong surname: Niu fullname: Niu, Xinsong organization: School of Statistics, Dongbei University of Finance and Economics, Dalian, China – sequence: 2 givenname: Jiyang surname: Wang fullname: Wang, Jiyang email: wjiyang@yeah.net organization: Faculty of Information Technology, Macau University of Science and Technology, Macau, China |
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| Keywords | Data preprocessing strategy Combined model Multi-objective optimization algorithm Artificial intelligence Wind speed forecasting |
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| Snippet | •A multi-objective optimization algorithm is successfully developed.•Employ a data denoising strategy to process the raw wind speed sequence.•Propose a novel... Short-term wind speed forecasting plays an important role in wind power generation and considerably contributes to decisions regarding control and operations.... |
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| SubjectTerms | algorithms Artificial intelligence China Combined model Data preprocessing strategy grasshoppers linear models Multi-objective optimization algorithm power generation prediction wind farms wind power wind speed Wind speed forecasting |
| Title | A combined model based on data preprocessing strategy and multi-objective optimization algorithm for short-term wind speed forecasting |
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