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

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  • Ren, Xiaoying
  • Zhang, Fei
  • Zhu, Honglu
  • Liu, Yongqian

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.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922009801
    DOI: 10.1016/j.apenergy.2022.119682
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    as
    1. 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).
    2. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
    3. Raza, Muhammad Qamar & Khosravi, Abbas, 2015. "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1352-1372.
    4. Yagli, Gokhan Mert & Yang, Dazhi & Srinivasan, Dipti, 2019. "Automatic hourly solar forecasting using machine learning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 105(C), pages 487-498.
    5. Hodge, Bri-Mathias & Brancucci Martinez-Anido, Carlo & Wang, Qin & Chartan, Erol & Florita, Anthony & Kiviluoma, Juha, 2018. "The combined value of wind and solar power forecasting improvements and electricity storage," Applied Energy, Elsevier, vol. 214(C), pages 1-15.
    6. Cai, Mengmeng & Pipattanasomporn, Manisa & Rahman, Saifur, 2019. "Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques," Applied Energy, Elsevier, vol. 236(C), pages 1078-1088.
    7. Qu, Jiaqi & Qian, Zheng & Pei, Yan, 2021. "Day-ahead hourly photovoltaic power forecasting using attention-based CNN-LSTM neural network embedded with multiple relevant and target variables prediction pattern," Energy, Elsevier, vol. 232(C).
    8. Severiano, Carlos A. & Silva, Petrônio Cândido de Lima e & Weiss Cohen, Miri & Guimarães, Frederico Gadelha, 2021. "Evolving fuzzy time series for spatio-temporal forecasting in renewable energy systems," Renewable Energy, Elsevier, vol. 171(C), pages 764-783.
    9. Gangqiang Li & Huaizhi Wang & Shengli Zhang & Jiantao Xin & Huichuan Liu, 2019. "Recurrent Neural Networks Based Photovoltaic Power Forecasting Approach," Energies, MDPI, vol. 12(13), pages 1-17, July.
    10. Wang, Kejun & Qi, Xiaoxia & Liu, Hongda, 2019. "Photovoltaic power forecasting based LSTM-Convolutional Network," Energy, Elsevier, vol. 189(C).
    11. 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.
    12. Diagne, Maimouna & David, Mathieu & Lauret, Philippe & Boland, John & Schmutz, Nicolas, 2013. "Review of solar irradiance forecasting methods and a proposition for small-scale insular grids," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 65-76.
    13. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
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    Cited by:

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    4. Yongning Zhang & Xiaoying Ren & Fei Zhang & Yulei Liu & Jierui Li, 2024. "A Deep Learning-Based Dual-Scale Hybrid Model for Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(17), pages 1-22, August.
    5. Fei Zhang & Xiaoying Ren & Guidong Yang & Shulong Zhang & Yongqian Liu, 2024. "Optimization Method of Multi-Mode Model Predictive Control for Wind Farm Reactive Power," Energies, MDPI, vol. 17(6), pages 1-20, March.
    6. Xiaoying Ren & Yongqian Liu & Fei Zhang & Lingfeng Li, 2024. "A Deep Learning Quantile Regression Photovoltaic Power-Forecasting Method under a Priori Knowledge Injection," Energies, MDPI, vol. 17(16), pages 1-25, August.
    7. Niu, Yunbo & Wang, Jianzhou & Zhang, Ziyuan & Luo, Tianrui & Liu, Jingjiang, 2024. "De-Trend First, Attend Next: A Mid-Term PV forecasting system with attention mechanism and encoder–decoder structure," Applied Energy, Elsevier, vol. 353(PB).
    8. Fei Zhang & Xiaoying Ren & Yongqian Liu, 2024. "A Refined Wind Power Forecasting Method with High Temporal Resolution Based on Light Convolutional Neural Network Architecture," Energies, MDPI, vol. 17(5), pages 1-25, March.
    9. Yunzhu Gao & Jun Wang & Lin Guo & Hong Peng, 2024. "Short-Term Photovoltaic Power Prediction Using Nonlinear Spiking Neural P Systems," Sustainability, MDPI, vol. 16(4), pages 1-18, February.
    10. 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.
    11. Paletta, Quentin & Hu, Anthony & Arbod, Guillaume & Lasenby, Joan, 2022. "ECLIPSE: Envisioning CLoud Induced Perturbations in Solar Energy," Applied Energy, Elsevier, vol. 326(C).
    12. Xiaoying Ren & Fei Zhang & Junshuai Yan & Yongqian Liu, 2024. "A Novel Convolutional Neural Net Architecture Based on Incorporating Meteorological Variable Inputs into Ultra-Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 16(7), pages 1-21, March.

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