Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models
Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed...
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| Format: | Article |
| Language: | English |
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MDPI AG
2018-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/11/11/2976 |
| _version_ | 1828110015670517760 |
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| author | Qinkai Han Hao Wu Tao Hu Fulei Chu |
| author_facet | Qinkai Han Hao Wu Tao Hu Fulei Chu |
| author_sort | Qinkai Han |
| collection | DOAJ |
| description | Accurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed series is decomposed into a finite number of intrinsic mode functions (IMFs) and residuals by using the EMD. Several popular linear and nonlinear models, including autoregressive integrated moving average (ARIMA), support vector machine (SVM), random forest (RF), artificial neural network with back propagation (BP), extreme learning machines (ELM) and convolutional neural network (CNN), are utilized to study IMFs and residuals, respectively. An ensemble forecast for the original wind speed series is then obtained. Various experiments were conducted on real wind speed series at four wind sites in China. The performance and robustness of various hybrid linear/nonlinear models at two time intervals (10 min and 1 h) are compared comprehensively. It is shown that the EMD based hybrid linear/nonlinear models have better accuracy and more robust performance than the single models with/without EMD. Among the five hybrid models, EMD-ARIMA-RF has the best accuracy on the whole for 10 min data, and the mean absolute percentage error (MAPE) is less than 0.04. However, for the 1 h data, no model can always perform well on the four datasets, and the MAPE is around 0.15. |
| first_indexed | 2024-04-11T11:13:05Z |
| format | Article |
| id | doaj.art-a3d59e91dd134b2bad2678a063dc18f7 |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-11T11:13:05Z |
| publishDate | 2018-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-a3d59e91dd134b2bad2678a063dc18f72022-12-22T04:27:25ZengMDPI AGEnergies1996-10732018-11-011111297610.3390/en11112976en11112976Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear ModelsQinkai Han0Hao Wu1Tao Hu2Fulei Chu3The State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, ChinaSchool of Mathematical Sciences, Capital Normal University, Beijing 100048, ChinaSchool of Mathematical Sciences, Capital Normal University, Beijing 100048, ChinaThe State Key Laboratory of Tribology, Tsinghua University, Beijing 100084, ChinaAccurate wind speed forecasting is a significant factor in grid load management and system operation. The aim of this study is to propose a framework for more precise short-term wind speed forecasting based on empirical mode decomposition (EMD) and hybrid linear/nonlinear models. Original wind speed series is decomposed into a finite number of intrinsic mode functions (IMFs) and residuals by using the EMD. Several popular linear and nonlinear models, including autoregressive integrated moving average (ARIMA), support vector machine (SVM), random forest (RF), artificial neural network with back propagation (BP), extreme learning machines (ELM) and convolutional neural network (CNN), are utilized to study IMFs and residuals, respectively. An ensemble forecast for the original wind speed series is then obtained. Various experiments were conducted on real wind speed series at four wind sites in China. The performance and robustness of various hybrid linear/nonlinear models at two time intervals (10 min and 1 h) are compared comprehensively. It is shown that the EMD based hybrid linear/nonlinear models have better accuracy and more robust performance than the single models with/without EMD. Among the five hybrid models, EMD-ARIMA-RF has the best accuracy on the whole for 10 min data, and the mean absolute percentage error (MAPE) is less than 0.04. However, for the 1 h data, no model can always perform well on the four datasets, and the MAPE is around 0.15.https://www.mdpi.com/1996-1073/11/11/2976wind speed forecastinghybrid modelingEMDARIMAmachine learning models |
| spellingShingle | Qinkai Han Hao Wu Tao Hu Fulei Chu Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models Energies wind speed forecasting hybrid modeling EMD ARIMA machine learning models |
| title | Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models |
| title_full | Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models |
| title_fullStr | Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models |
| title_full_unstemmed | Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models |
| title_short | Short-Term Wind Speed Forecasting Based on Signal Decomposing Algorithm and Hybrid Linear/Nonlinear Models |
| title_sort | short term wind speed forecasting based on signal decomposing algorithm and hybrid linear nonlinear models |
| topic | wind speed forecasting hybrid modeling EMD ARIMA machine learning models |
| url | https://www.mdpi.com/1996-1073/11/11/2976 |
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