Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm
•Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting.•Improve the accuracy of multi-step wind speed forecasting.•Modify bat algorithm with CG to improve optimized performance. As one of the most promising sustainable energy sources, wind energy plays...
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| Published in | Energy conversion and management Vol. 143; pp. 410 - 430 |
|---|---|
| Main Authors | , , |
| Format | Journal Article |
| Language | English |
| Published |
Oxford
Elsevier Ltd
01.07.2017
Elsevier Science Ltd |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0196-8904 1879-2227 |
| DOI | 10.1016/j.enconman.2017.04.012 |
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| Abstract | •Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting.•Improve the accuracy of multi-step wind speed forecasting.•Modify bat algorithm with CG to improve optimized performance.
As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting. |
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| AbstractList | As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting. •Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting.•Improve the accuracy of multi-step wind speed forecasting.•Modify bat algorithm with CG to improve optimized performance. As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing pollution. Generally, wind speed forecasting, which has an essential influence on wind power systems, is regarded as a challenging task. Analyses based on single-step wind speed forecasting have been widely used, but their results are insufficient in ensuring the reliability and controllability of wind power systems. In this paper, a new forecasting architecture based on decomposing algorithms and modified neural networks is successfully developed for multi-step wind speed forecasting. Four different hybrid models are contained in this architecture, and to further improve the forecasting performance, a modified bat algorithm (BA) with the conjugate gradient (CG) method is developed to optimize the initial weights between layers and thresholds of the hidden layer of neural networks. To investigate the forecasting abilities of the four models, the wind speed data collected from four different wind power stations in Penglai, China, were used as a case study. The numerical experiments showed that the hybrid model including the singular spectrum analysis and general regression neural network with CG-BA (SSA-CG-BA-GRNN) achieved the most accurate forecasting results in one-step to three-step wind speed forecasting. |
| Author | Shao, Wei Qian, Feng Xiao, Liye |
| Author_xml | – sequence: 1 givenname: Liye surname: Xiao fullname: Xiao, Liye organization: School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu, China – sequence: 2 givenname: Feng surname: Qian fullname: Qian, Feng organization: Department of Electronics Engineering and Computer Science, Peking University, Beijing, China – sequence: 3 givenname: Wei surname: Shao fullname: Shao, Wei email: weishao@uestc.edu.cn organization: School of Physical Electronics, University of Electronic Science and Technology of China, Chengdu, China |
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| Keywords | ANN FEEMD GRNN WPD SVM WD Hybrid forecasting architecture CSA SDA EEMD GA EA EMD MSE Improved bat algorithm SSA CG MAPE Singular spectrum analysis, wind speed forecasting PSO MAE RBFNN FVD ARIMA BA |
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| Snippet | •Propose a hybrid architecture based on a modified bat algorithm for multi-step wind speed forecasting.•Improve the accuracy of multi-step wind speed... As one of the most promising sustainable energy sources, wind energy plays an important role in energy development because of its cleanliness without causing... |
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| SubjectTerms | Algorithms Architecture case studies China Chiroptera Controllability data collection energy Energy sources Forecasting General regression neural networks Hybrid forecasting architecture Improved bat algorithm Mathematical models Neural networks pollution Power plants Regression analysis Singular spectrum analysis, wind speed forecasting Spectrum analysis Sustainable energy Weather forecasting Wind power Wind speed |
| Title | Multi-step wind speed forecasting based on a hybrid forecasting architecture and an improved bat algorithm |
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