Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach

doi:10.1016/j.energy.2022.125609

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	Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach
	Yang, Dongchuan 1; Guo, Ju-e 1; Li, Yanzhao 1; Sun, Shaolong 1; Wang, Shouyang 2,3
	2023-01-15
发表期刊	ENERGY
ISSN	0360-5442
卷号	263 页码:16
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.
关键词	Short -term load forecasting Time series modeling Dynamic decomposition-reconstruction tech nique Neural networks
DOI	10.1016/j.energy.2022.125609
收录类别	SCI
语种	英语
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
WOS类目	Thermodynamics ; Energy & Fuels
WOS记录号	WOS:000868319200003
出版者	PERGAMON-ELSEVIER SCIENCE LTD
引用统计
文献类型	期刊论文
条目标识符	http://ir.amss.ac.cn/handle/2S8OKBNM/60747
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.Xi An Jiao Tong Univ, Sch Management, Xian 710049, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Beijing 100190, 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.