A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm
文献类型：期刊论文
| 作者 | Wang, Lu2; Liao, Yilan |
| 刊名 | FRONTIERS IN ENERGY RESEARCH
 |
| 出版日期 | 2023-11-21 |
| 卷号 | 11页码:1298088 |
| 关键词 | variational modal decomposition attention long short-term memory salp swarm algorithm short-term wind speed prediction |
| DOI | 10.3389/fenrg.2023.1298088 |
| 产权排序 | 1 |
| 文献子类 | Article |
| 英文摘要 | Introduction: In the field of wind power generation, short-term wind speed prediction plays an increasingly important role as the foundation for effective utilization of wind energy. However, accurately predicting wind speed is highly challenging due to its complexity and randomness in practical applications. Currently, single algorithms exhibit poor accuracy in short-term wind speed prediction, leading to the widespread adoption of hybrid wind speed prediction models based on deep learning techniques. To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization.Methods: To comprehensively enhance the predictive performance of short-term wind speed models, this study proposes a hybrid model, VMDAttention LSTM-ASSA, which consists of three stages: decomposition of the original wind speed sequence, prediction of each mode component, and weight optimization. Firstly, the model incorporates an attention mechanism into the LSTM model to extract important temporal slices from each mode component, effectively improving the slice prediction accuracy. Secondly, two different search operators are introduced to enhance the original Salp Swarm Algorithm, addressing the issue of getting trapped in local optima and achieving globally optimal short-term wind speed predictions.Result: Through comparative experiments using multiple-site short-term wind speed datasets, this study demonstrates that the proposed VMD-AtLSTM-ASSA model outperforms other hybrid prediction models (VMD-RNN, VMD-BPNN, VMD-GRU, VMD-LSTM) with a maximum reduction of 80.33% in MAPE values. The experimental results validate the high accuracy and stability of the VMD-AtLSTM-ASSA model.Discussion: Short-term wind speed prediction is of paramount importance for the effective utilization of wind power generation, and our research provides strong support for enhancing the efficiency and reliability of wind power generation systems. Future research directions may include further improvements in model performance and extension into other meteorological and environmental application domains. |
| WOS关键词 | SALP SWARM ALGORITHM ; DIFFERENTIAL EVOLUTION ; IMPACT |
| WOS研究方向 | Energy & Fuels |
| WOS记录号 | WOS:001117796100001 |
| 出版者 | FRONTIERS MEDIA SA |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/200997]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 作者单位 | 1.Guangxi Univ Sci & Technol, Sch Sci, Liuzhou, Peoples R China 2.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing, Peoples R China |
| 推荐引用方式 GB/T 7714 | Wang, Lu,Liao, Yilan. A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm[J]. FRONTIERS IN ENERGY RESEARCH,2023,11:1298088. |
| APA | Wang, Lu,&Liao, Yilan.(2023).A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm.FRONTIERS IN ENERGY RESEARCH,11,1298088. |
| MLA | Wang, Lu,et al."A short-term hybrid wind speed prediction model based on decomposition and improved optimization algorithm".FRONTIERS IN ENERGY RESEARCH 11(2023):1298088. |
入库方式： OAI收割
来源：地理科学与资源研究所
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