Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme
Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind s...
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| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2020-01-01
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| Series: | Complexity |
| Online Access: | http://dx.doi.org/10.1155/2020/9601763 |
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| _version_ | 1849472934852165632 |
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| author | Ying Nie He Bo Weiqun Zhang Haipeng Zhang |
| author_facet | Ying Nie He Bo Weiqun Zhang Haipeng Zhang |
| author_sort | Ying Nie |
| collection | DOAJ |
| description | Wind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. The experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning. |
| format | Article |
| id | doaj-art-db912bd2d1ea4460a17c5bf5e38317c9 |
| institution | Kabale University |
| issn | 1076-2787 1099-0526 |
| language | English |
| publishDate | 2020-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Complexity |
| spelling | doaj-art-db912bd2d1ea4460a17c5bf5e38317c92025-08-20T03:24:21ZengWileyComplexity1076-27871099-05262020-01-01202010.1155/2020/96017639601763Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction SchemeYing Nie0He Bo1Weiqun Zhang2Haipeng Zhang3School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaPostdoctoral Research Station, Dongbei University of Finance and Economics, Dalian 116025, ChinaSchool of Statistics, Xi’an University of Finance and Economics, Xi’an 710100, ChinaSchool of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaWind energy analysis and wind speed modeling have a significant impact on wind power generation systems and have attracted significant attention from many researchers in recent decades. Based on the inherent characteristics of wind speed, such as nonlinearity and randomness, the prediction of wind speed is considered to be a challenging task. Previous studies have only considered point prediction or interval measurement of wind speed separately and have not combined these two methods for prediction and analysis. In this study, we developed a novel hybrid wind speed double prediction system comprising a point prediction module and interval prediction module to compensate for the shortcomings of existing research. Regarding point prediction in the developed double prediction system, a novel nonlinear integration method based on a backpropagation network optimized using the multiobjective evolutionary algorithm based on decomposition was successfully implemented to derive the final prediction results, which enable further improvement of the accuracy of point prediction. Based on point prediction results, we propose an interval prediction method that constructs different intervals according to the classification of different data features via fuzzy clustering, which provides reliable interval prediction results. The experimental results demonstrate that the proposed system outperforms existing methods in engineering applications and can be used as an effective technology for power system planning.http://dx.doi.org/10.1155/2020/9601763 |
| spellingShingle | Ying Nie He Bo Weiqun Zhang Haipeng Zhang Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme Complexity |
| title | Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme |
| title_full | Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme |
| title_fullStr | Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme |
| title_full_unstemmed | Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme |
| title_short | Research on Hybrid Wind Speed Prediction System Based on Artificial Intelligence and Double Prediction Scheme |
| title_sort | research on hybrid wind speed prediction system based on artificial intelligence and double prediction scheme |
| url | http://dx.doi.org/10.1155/2020/9601763 |
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