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DOI	10.1016/j.apenergy.2021.117410
	SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting
	Korkmaz, Deniz
通讯作者	Korkmaz, D (corresponding author), Malatya Turgut Ozal Univ, Fac Engn & Nat Sci, Dept Elect & Elect Engn, TR-44210 Malatya, Turkey.
来源期刊	APPLIED ENERGY
ISSN	0306-2619
EISSN	1872-9118
出版年	2021
卷号	300
英文摘要	Photovoltaic (PV) power generation has high uncertainties due to the randomness and imbalance nature of solar energy and meteorological parameters. Hence, accurate PV power forecasts are essential in the operation of PV power plants (PVPP) for short-term dispatches and power generation schedules. In this study, a novel convolutional neural network (CNN) model, namely SolarNet, is proposed for short-term PV output power forecasting under different weather conditions and seasons. The proposed CNN model is designed as a parallel pooling structure to increase the forecasting performance. This structure consists of max-pooling and averagepooling blocks. The input parameters are the measured historical solar radiation, temperature, humidity, and active power data. The power data is decomposed into sub-components with the variational mode decomposition method and a data preprocessing and reconstruction process is utilized to obtain deep input feature maps. After input parameters are converted to hue-saturation-value (HSV) color space, the subsets feed to the input of the network. The experimental studies are performed with a case study using a 23.40 kW PVPP dataset from the Desert Knowledge Australia Solar Centre. The design CNN model is also compared with benchmark deep learning methods. In the experiments, the average correlation coefficient (R), root mean square error (RMSE), and mean absolute error (MAE) of the proposed method for 1-h different weather conditions are achieved as 0.9871, 0.3090, and 0.1750, respectively. The experimental results show that the proposed deep forecasting method has higher accuracy and stability in short-term PV power forecasting and outperforms the other deep learning methods.
英文关键词	Photovoltaic power forecasting Convolutional neural network Parallel pooling Variational mode decomposition Deep learning
类型	Article
语种	英语
收录类别	SCI-E
WOS记录号	WOS:000684860700005
WOS关键词	GENERATION ; PREDICTION ; OUTPUT
WOS类目	Energy & Fuels ; Engineering, Chemical
WOS研究方向	Energy & Fuels ; Engineering
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/362478
作者单位	[Korkmaz, Deniz] Malatya Turgut Ozal Univ, Fac Engn & Nat Sci, Dept Elect & Elect Engn, TR-44210 Malatya, Turkey
推荐引用方式 GB/T 7714	Korkmaz, Deniz. SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting[J],2021,300.
APA	Korkmaz, Deniz.(2021).SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting.APPLIED ENERGY,300.
MLA	Korkmaz, Deniz."SolarNet: A hybrid reliable model based on convolutional neural network and variational mode decomposition for hourly photovoltaic power forecasting".APPLIED ENERGY 300(2021).

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