A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting
With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for e...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2016-09-01
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| Series: | Energies |
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| Online Access: | http://www.mdpi.com/1996-1073/9/10/757 |
| _version_ | 1828392530252660736 |
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| author | Yuyang Gao Chao Qu Kequan Zhang |
| author_facet | Yuyang Gao Chao Qu Kequan Zhang |
| author_sort | Yuyang Gao |
| collection | DOAJ |
| description | With increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability. |
| first_indexed | 2024-12-10T07:22:47Z |
| format | Article |
| id | doaj.art-62c8ad1cf0554859961aaf1c6c346169 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-12-10T07:22:47Z |
| publishDate | 2016-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-62c8ad1cf0554859961aaf1c6c3461692022-12-22T01:57:47ZengMDPI AGEnergies1996-10732016-09-0191075710.3390/en9100757en9100757A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed ForecastingYuyang Gao0Chao Qu1Kequan Zhang2School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaSchool of Statistics, Dongbei University of Finance and Economics, Dalian 116025, ChinaKey Laboratory of Arid Climatic Change and Reducing Disaster of Gansu Province, College of Atmospheric Sciences, Lanzhou University, Lanzhou 730000, ChinaWith increasing importance being attached to big data mining, analysis, and forecasting in the field of wind energy, how to select an optimization model to improve the forecasting accuracy of the wind speed time series is not only an extremely challenging problem, but also a problem of concern for economic forecasting. The artificial intelligence model is widely used in forecasting and data processing, but the individual back-propagation artificial neural network cannot always satisfy the time series forecasting needs. Thus, a hybrid forecasting approach has been proposed in this study, which consists of data preprocessing, parameter optimization and a neural network for advancing the accuracy of short-term wind speed forecasting. According to the case study, in which the data are collected from Peng Lai, a city located in China, the simulation results indicate that the hybrid forecasting method yields better predictions compared to the individual BP, which indicates that the hybrid method exhibits stronger forecasting ability.http://www.mdpi.com/1996-1073/9/10/757hybrid methodshort-term wind speed series forecastingforecasting accuracyneural networkartificial intelligenceoptimization algorithm |
| spellingShingle | Yuyang Gao Chao Qu Kequan Zhang A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting Energies hybrid method short-term wind speed series forecasting forecasting accuracy neural network artificial intelligence optimization algorithm |
| title | A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting |
| title_full | A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting |
| title_fullStr | A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting |
| title_full_unstemmed | A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting |
| title_short | A Hybrid Method Based on Singular Spectrum Analysis, Firefly Algorithm, and BP Neural Network for Short-Term Wind Speed Forecasting |
| title_sort | hybrid method based on singular spectrum analysis firefly algorithm and bp neural network for short term wind speed forecasting |
| topic | hybrid method short-term wind speed series forecasting forecasting accuracy neural network artificial intelligence optimization algorithm |
| url | http://www.mdpi.com/1996-1073/9/10/757 |
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