Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm
Accurate wind speed forecasting is important for the reliable and efficient operation of the wind power system. The present study investigated singular spectrum analysis (SSA) with a reduced parameter algorithm in three time series models, the autoregressive integrated moving average (ARIMA) model,...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Wiley
2014-01-01
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| Series: | Abstract and Applied Analysis |
| Online Access: | http://dx.doi.org/10.1155/2014/693205 |
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| author | Wenyu Zhang Zhongyue Su Hongli Zhang Yanru Zhao Zhiyuan Zhao |
| author_facet | Wenyu Zhang Zhongyue Su Hongli Zhang Yanru Zhao Zhiyuan Zhao |
| author_sort | Wenyu Zhang |
| collection | DOAJ |
| description | Accurate wind speed forecasting is important for the reliable and efficient operation of the wind power system. The present study investigated singular spectrum analysis (SSA) with a reduced parameter algorithm in three time series models, the autoregressive integrated moving average (ARIMA) model, the support vector machine (SVM) model, and the artificial neural network (ANN) model, to forecast the wind speed in Shandong province, China. In the proposed model, the weather research and forecasting model (WRF) is first employed as a physical background to provide the elements of weather data. To reduce these noises, SSA is used to develop a self-adapting parameter selection algorithm that is fully data-driven. After optimization, the SSA-based forecasting models are applied to forecasting the immediate short-term wind speed and are adopted at ten wind farms in China. Finally, the performance of the proposed approach is evaluated using observed data according to three error calculation methods. The simulation results from ten cases show that the proposed method has better forecasting performance than the traditional methods. |
| format | Article |
| id | doaj-art-9f1f08479f9647b58f8e0c494fca83b5 |
| institution | Kabale University |
| issn | 1085-3375 1687-0409 |
| language | English |
| publishDate | 2014-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Abstract and Applied Analysis |
| spelling | doaj-art-9f1f08479f9647b58f8e0c494fca83b52025-02-03T01:23:47ZengWileyAbstract and Applied Analysis1085-33751687-04092014-01-01201410.1155/2014/693205693205Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized AlgorithmWenyu Zhang0Zhongyue Su1Hongli Zhang2Yanru Zhao3Zhiyuan Zhao4Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, ChinaKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, ChinaKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, ChinaKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, ChinaKey Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Key Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, Lanzhou 730000, ChinaAccurate wind speed forecasting is important for the reliable and efficient operation of the wind power system. The present study investigated singular spectrum analysis (SSA) with a reduced parameter algorithm in three time series models, the autoregressive integrated moving average (ARIMA) model, the support vector machine (SVM) model, and the artificial neural network (ANN) model, to forecast the wind speed in Shandong province, China. In the proposed model, the weather research and forecasting model (WRF) is first employed as a physical background to provide the elements of weather data. To reduce these noises, SSA is used to develop a self-adapting parameter selection algorithm that is fully data-driven. After optimization, the SSA-based forecasting models are applied to forecasting the immediate short-term wind speed and are adopted at ten wind farms in China. Finally, the performance of the proposed approach is evaluated using observed data according to three error calculation methods. The simulation results from ten cases show that the proposed method has better forecasting performance than the traditional methods.http://dx.doi.org/10.1155/2014/693205 |
| spellingShingle | Wenyu Zhang Zhongyue Su Hongli Zhang Yanru Zhao Zhiyuan Zhao Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm Abstract and Applied Analysis |
| title | Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm |
| title_full | Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm |
| title_fullStr | Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm |
| title_full_unstemmed | Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm |
| title_short | Hybrid Wind Speed Forecasting Model Study Based on SSA and Intelligent Optimized Algorithm |
| title_sort | hybrid wind speed forecasting model study based on ssa and intelligent optimized algorithm |
| url | http://dx.doi.org/10.1155/2014/693205 |
| work_keys_str_mv | AT wenyuzhang hybridwindspeedforecastingmodelstudybasedonssaandintelligentoptimizedalgorithm AT zhongyuesu hybridwindspeedforecastingmodelstudybasedonssaandintelligentoptimizedalgorithm AT honglizhang hybridwindspeedforecastingmodelstudybasedonssaandintelligentoptimizedalgorithm AT yanruzhao hybridwindspeedforecastingmodelstudybasedonssaandintelligentoptimizedalgorithm AT zhiyuanzhao hybridwindspeedforecastingmodelstudybasedonssaandintelligentoptimizedalgorithm |