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A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting

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  • Xiaoying Ren

    (School of Renewable Energy, North China Electric Power University, Beijing 100000, China
    College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Fei Zhang

    (School of Renewable Energy, North China Electric Power University, Beijing 100000, China
    College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yongrui Sun

    (College of Information Engineering, Inner Mongolia University of Science and Technology, Baotou 014010, China)

  • Yongqian Liu

    (School of Renewable Energy, North China Electric Power University, Beijing 100000, China)

Abstract

A large proportion of photovoltaic (PV) power generation is connected to the power grid, and its volatility and stochasticity have significant impacts on the power system. Accurate PV power forecasting is of great significance in optimizing the safe operation of the power grid and power market transactions. In this paper, a novel dual-channel PV power forecasting method based on a temporal convolutional network (TCN) is proposed. The method deeply integrates the PV station feature data with the model computing mechanism through the dual-channel model architecture; utilizes the combination of multihead attention (MHA) and TCN to extract the multidimensional spatio-temporal features between other meteorological variables and the PV power; and utilizes a single TCN to fully extract the temporal constraints of the power sequence elements. The weighted fusion of the dual-channel feature data ultimately yields the ideal forecasting results. The experimental data in this study are from a 26.52 kW PV power plant in central Australia. The experiments were carried out over seven different input window widths, and the two models that currently show superior performance within the field of PV power forecasting: the convolutional neural network (CNN), and the convolutional neural network combined with a long and short-term memory network (CNN_LSTM), are used as the baseline models. The experimental results show that the proposed model and the baseline models both obtained the best forecasting performance over a 1-day input window width, while the proposed model exhibited superior forecasting performance compared to the baseline model. It also shows that designing model architectures that deeply integrate the data input method with the model mechanism has research potential in the field of PV power forecasting.

Suggested Citation

  • Xiaoying Ren & Fei Zhang & Yongrui Sun & Yongqian Liu, 2024. "A Novel Dual-Channel Temporal Convolutional Network for Photovoltaic Power Forecasting," Energies, MDPI, vol. 17(3), pages 1-19, February.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:698-:d:1331070
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    References listed on IDEAS

    as
    1. Limouni, Tariq & Yaagoubi, Reda & Bouziane, Khalid & Guissi, Khalid & Baali, El Houssain, 2023. "Accurate one step and multistep forecasting of very short-term PV power using LSTM-TCN model," Renewable Energy, Elsevier, vol. 205(C), pages 1010-1024.
    2. Ren, Xiaoying & Zhang, Fei & Zhu, Honglu & Liu, Yongqian, 2022. "Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting," Applied Energy, Elsevier, vol. 323(C).
    3. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
    4. Li, Pengtao & Zhou, Kaile & Lu, Xinhui & Yang, Shanlin, 2020. "A hybrid deep learning model for short-term PV power forecasting," Applied Energy, Elsevier, vol. 259(C).
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