1. Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting.
- Author
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Ren, Xiaoying, Zhang, Fei, Zhu, Honglu, and Liu, Yongqian
- Subjects
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ARTIFICIAL neural networks , *CONVOLUTIONAL neural networks , *FORECASTING , *FEATURE extraction , *DEEP learning , *MACHINE learning - Abstract
• A quad-kernel convolutional neural network model for PV power forecasting. • Sequence-to-sequence learning method to capture timing-dependent features. • Multiple global-max-pooling algorithms to improve the learning efficiency. • The effect of dataset resolution on the model performance is studied. • Operation data prove the higher forecasting accuracy of the proposed model. Photovoltaic (PV) power is highly stochastic and volatile, and PV power forecasting is a key technology to guarantee the safe and economic operation of high-penetration renewable power systems. To improve the accuracy of PV power forecasting, a quad-kernel deep convolutional neural network (QK_CNN) model is proposed to perform intra-hour PV power forecasting for the next four timesteps: four CNNs with different kernel sizes are used to extract different local cross features between sequence elements of four timesteps; a single-kernel CNN is used to further feature extraction of these features, and then the target sequence forecasting results are obtained; global maximum pooling method is used to simplify the feature extraction process and improve model learning efficiency. Operation data from a 26.52 kW PV plant in Central Australia is selected as the experimental data. Compared with single-kernel CNN and hybrid models (CNN_LSTM) on 5, 10, and 15 min of resolution data, respectively, the proposed model shows better forecasting performance and is able to explain 96 % to 98 % of the total variation of the forecasted PV power. All these demonstrates that the CNNs with specific design have great potential to handle the task of PV power forecasting as well. [ABSTRACT FROM AUTHOR]
- Published
- 2022
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