气候变化领域集成服务门户(CCPortal): A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting

CCPortal

DOI	10.3390/su16072786
	A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting
	Ren, Xiaoying; Zhang, Fei; Yan, Junshuai; Liu, Yongqian
发表日期	2024
EISSN	2071-1050
起始页码	16
结束页码	7
卷号	16 期号:7
英文摘要	Accurate photovoltaic (PV) power forecasting allows for better integration and management of renewable energy sources, which can help to reduce our dependence on finite fossil fuels, drive energy transitions and climate change mitigation, and thus promote the sustainable development of renewable energy sources. A convolutional neural network (CNN) forecasting method with a two-input, two-scale parallel cascade structure is proposed for ultra-short-term PV power forecasting tasks. The dual-input pattern of the model is constructed by integrating the weather variables and the historical power so as to convey finer information about the interaction between the weather variables and the PV power to the model; the design of the two-branch, two-scale CNN model architecture realizes in-depth fusion of the PV system data with the CNN's feature extraction mechanism. Each branch introduces an attention mechanism (AM) that focuses on the degree of influence between elements within the historical power sequence and the degree of influence of each meteorological variable on the historical power sequence, respectively. Actual operational data from three PV plants under different meteorological conditions are used. Compared with the baseline model, the proposed model shows a better forecasting performance, which provides a new idea for deep-learning-based PV power forecasting techniques, as well as important technical support for a high percentage of PV energy to be connected to the grid, thus promoting the sustainable development of renewable energy.
英文关键词	photovoltaic power forecasting; dual input; deep learning; attention mechanism; convolutional neural network
语种	英语
WOS研究方向	Science & Technology - Other Topics ; Environmental Sciences & Ecology
WOS类目	Green & Sustainable Science & Technology ; Environmental Sciences ; Environmental Studies
WOS记录号	WOS:001200951500001
来源期刊	SUSTAINABILITY
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/298411
作者单位	North China Electric Power University; Inner Mongolia University of Science & Technology
推荐引用方式 GB/T 7714	Ren, Xiaoying,Zhang, Fei,Yan, Junshuai,et al. A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting[J],2024,16(7).
APA	Ren, Xiaoying,Zhang, Fei,Yan, Junshuai,&Liu, Yongqian.(2024).A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting.SUSTAINABILITY,16(7).
MLA	Ren, Xiaoying,et al."A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting".SUSTAINABILITY 16.7(2024).