Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach
文献类型：期刊论文
| 作者 | Yang, Dongchuan2; Guo, Ju-e2; Li, Yanzhao2; Sun, Shaolong2; Wang, Shouyang1,3 |
| 刊名 | ENERGY
 |
| 出版日期 | 2023-01-15 |
| 卷号 | 263页码:16 |
| 关键词 | Short -term load forecasting Time series modeling Dynamic decomposition-reconstruction tech nique Neural networks |
| ISSN号 | 0360-5442 |
| DOI | 10.1016/j.energy.2022.125609 |
| 英文摘要 | Short-term load forecasting has evolved into an important aspect of power system in safe operation and rational dispatching. However, given the load series' instability and volatility, this is a challenging task. To this end, this study proposes a dynamic decomposition-reconstruction-ensemble approach by cleverly and dynamically combining two proven and effective techniques (i.e., the reconstruction techniques and the secondary decom-position techniques). In fact, by introducing the decomposition-reconstruction process based on the dynamic classification, filtering, and giving the criteria for determining the components that need to be decomposed again, our proposed model improves the decomposition-ensemble forecasting framework. Our proposed model makes full use of decomposition techniques, complexity analysis, reconstruction techniques, secondary decom-position techniques, and a neural network optimized by an automatic hyperparameter optimization algorithm. Besides, we compared our proposed model with state-of-the-art models including five models with reconstruction techniques and two models with secondary decomposition techniques. The experiment results demonstrate the superiority of our proposed dynamic decomposition-reconstruction technique in terms of forecasting accuracy, precise direction, equality, stability, correlation, comprehensive accuracy, and statistical tests. To conclude, our proposed model has the potential to be a useful tool for short-term load forecasting. |
| 资助项目 | National Natural Science Foundation of China[71774130] ; National Natural Science Foundation of China[72101197] ; National Natural Science Foundation of China[71988101] ; Fundamental Research Funds for the Central Universities[SK2021007] ; Fundamental Research Funds for the Central Universities[SK2022040] ; Soft science project of Shaanxi Province[2022KRM093] ; China Postdoctoral Science Foundation[2021M702579] |
| WOS研究方向 | Thermodynamics ; Energy & Fuels |
| 语种 | 英语 |
| WOS记录号 | WOS:000868319200003 |
| 出版者 | PERGAMON-ELSEVIER SCIENCE LTD |
| 源URL | [http://ir.amss.ac.cn/handle/2S8OKBNM/60746]  |
| 专题 | 中国科学院数学与系统科学研究院 |
| 通讯作者 | Sun, Shaolong |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, Peoples R China 2.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China 3.Chinese Acad Sci, Ctr Forecasting Sci, Beijing 100190, Peoples R China |
| 推荐引用方式 GB/T 7714 | Yang, Dongchuan,Guo, Ju-e,Li, Yanzhao,et al. Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach[J]. ENERGY,2023,263:16. |
| APA | Yang, Dongchuan,Guo, Ju-e,Li, Yanzhao,Sun, Shaolong,&Wang, Shouyang.(2023).Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach.ENERGY,263,16. |
| MLA | Yang, Dongchuan,et al."Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach".ENERGY 263(2023):16. |
入库方式： OAI收割
来源：数学与系统科学研究院
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