干旱区生态环境知识资源中心(Arid): Prediction of short-term photovoltaic power via codec neural network and mode decomposition based deep learning approach

Arid

DOI	10.1002/ese3.1009
	Prediction of short-term photovoltaic power via codec neural network and mode decomposition based deep learning approach
	Li, Jie; Li, RunRan; Jia, YuanJie; Zhang, ZhiXin
通讯作者	Li, J (corresponding author), Changan Univ, Sch Elect & Control Engn, Xian 710064, Peoples R China.
来源期刊	ENERGY SCIENCE & ENGINEERING
EISSN	2050-0505
出版年	2021-11
英文摘要	Accurate photovoltaic (PV) power prediction is of great significance for the stable operation of PV system, but the PV power sequence is nonstationary, so it is difficult to establish the prediction model effectively by a simple neural network. In this study, the MCVMD-MI-SWATS-Codec (multidimensional constraints variational mode decomposition-mixed initialization-switching from Adam to stochastic gradient descent-codec) that is based on the idea of deep model fusion is proposed to predict PV power generation. MCVMD method with parameter K determined by multidimensional constraint criterion is used to decompose the PV power data, and the frequency of each component sequence is analyzed after decomposition to explore the physical characteristics and application value of each component frequency. Then, a hybrid ResNet-LSTM (residual network-long- and short-term memory) model based on codec mechanism integrates input data with different dimensions, such as weather conditions and historical IMF (intrinsic mode function), into dense vectors with the same dimension. The experimental data of polysilicon PV array in the Australian desert environment are used to test the proposed fusion neural network model and the other six competitive models. The results show that MCVMD algorithm is significantly helpful in decomposing the nonstationary data to improve the prediction accuracy, and MCVMD-MI-SWATS-Codec model has high prediction accuracy and robustness in both stable and unstable weather conditions.
英文关键词	codec neural network fusion neural network model multidimensional constraints variational mode decomposition photovoltaic power prediction
类型	Article ; Early Access
语种	英语
开放获取类型	gold
收录类别	SCI-E
WOS记录号	WOS:000720141500001
WOS关键词	SOLAR ; ENERGY ; MULTISTEP ; FORECASTS ; MACHINE
WOS类目	Energy & Fuels
WOS研究方向	Energy & Fuels
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/374603
作者单位	[Li, Jie; Li, RunRan; Jia, YuanJie; Zhang, ZhiXin] Changan Univ, Sch Elect & Control Engn, Xian 710064, Peoples R China
推荐引用方式 GB/T 7714	Li, Jie,Li, RunRan,Jia, YuanJie,et al. Prediction of short-term photovoltaic power via codec neural network and mode decomposition based deep learning approach[J],2021.
APA	Li, Jie,Li, RunRan,Jia, YuanJie,&Zhang, ZhiXin.(2021).Prediction of short-term photovoltaic power via codec neural network and mode decomposition based deep learning approach.ENERGY SCIENCE & ENGINEERING.
MLA	Li, Jie,et al."Prediction of short-term photovoltaic power via codec neural network and mode decomposition based deep learning approach".ENERGY SCIENCE & ENGINEERING (2021).