Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting

Xiaoying Ren, Fei Zhang, Honglu Zhu and Yongqian Liu

Applied Energy, 2022, vol. 323, issue C, No S0306261922009801

Abstract: 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 CentralAustralia 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.

Keywords: Photovoltaic power forecasting; Sequence-to-sequence; Global-max-pooling; Convolutional neural network; Deep learning (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (14)

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DOI: 10.1016/j.apenergy.2022.119682

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