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A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting

Author

Listed:
  • Huang, Xiaoqiao
  • Liu, Jun
  • Xu, Shaozhen
  • Li, Chengli
  • Li, Qiong
  • Tai, Yonghang

Abstract

Due to the intermittency and fluctuation of solar energy, its exponential growth presents serious challenges to the power system. Therefore, photovoltaic (PV) power forecasting, including solar irradiance forecasting, has become a necessary prerequisite for the grid connection of photovoltaic power stations. However, traditional 2D convolution networks are less effective in extracting spatial features, especially limited in handling long-term dependencies. To address these problems, in this paper, a novel ultra-short-term solar irradiance forecasting method based on a 3D Convolutional Long Short-Term Memory and 3D Convolutional Neural Networks (3D ConvLSTM-CNN) hybrid model is proposed by processing multiple consecutive all-sky images with various color channels, the spatial information of different color channel images can better extract different types of cloud information, and the 3D ConvLSTM-CNN can take into account the temporal information. The temporal and spatial features of the sky image are extracted from multiple images at different times, simultaneously, and the textual meteorological features of the corresponding images fused via the LSTM hybrid network input model to finally establish the model for forecasting the next moment. All-sky image data and irradiance data collected by Yunnan Normal University are used to test and verify the model. The experimental results indicate that the proposed method has a promising performance and achieves 28.2%, 34.8%, 19.9%, 42.7%, and 68.3% improvement on nRMSE, MAPE, SMAPE, MedAPE, and R2 over the persistence model for 5-min ahead global horizontal irradiance (GHI) prediction.

Suggested Citation

  • Huang, Xiaoqiao & Liu, Jun & Xu, Shaozhen & Li, Chengli & Li, Qiong & Tai, Yonghang, 2023. "A 3D ConvLSTM-CNN network based on multi-channel color extraction for ultra-short-term solar irradiance forecasting," Energy, Elsevier, vol. 272(C).
    • Handle: RePEc:eee:energy:v:272:y:2023:i:c:s0360544223005340
    DOI: 10.1016/j.energy.2023.127140
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    1. Chujie Tian & Jian Ma & Chunhong Zhang & Panpan Zhan, 2018. "A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network," Energies, MDPI, vol. 11(12), pages 1-13, December.
    2. Xiong, Wei & Liu, Zhongbing & Wu, Zhenghong & Wu, Jing & Su, Fanghan & Zhang, Ling, 2022. "Investigation of the effect of Inter-Building Effect on the performance of semi-transparent PV glazing system," Energy, Elsevier, vol. 245(C).
    3. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
    4. Huang, Xiaoqiao & Li, Qiong & Tai, Yonghang & Chen, Zaiqing & Liu, Jun & Shi, Junsheng & Liu, Wuming, 2022. "Time series forecasting for hourly photovoltaic power using conditional generative adversarial network and Bi-LSTM," Energy, Elsevier, vol. 246(C).
    5. Wang, Fei & Lu, Xiaoxing & Mei, Shengwei & Su, Ying & Zhen, Zhao & Zou, Zubing & Zhang, Xuemin & Yin, Rui & Duić, Neven & Shafie-khah, Miadreza & Catalão, João P.S., 2022. "A satellite image data based ultra-short-term solar PV power forecasting method considering cloud information from neighboring plant," Energy, Elsevier, vol. 238(PC).
    6. Chu, Yinghao & Li, Mengying & Pedro, Hugo T.C. & Coimbra, Carlos F.M., 2015. "Real-time prediction intervals for intra-hour DNI forecasts," Renewable Energy, Elsevier, vol. 83(C), pages 234-244.
    7. Wang, Xiaoyang & Sun, Yunlin & Luo, Duo & Peng, Jinqing, 2022. "Comparative study of machine learning approaches for predicting short-term photovoltaic power output based on weather type classification," Energy, Elsevier, vol. 240(C).
    8. Feng, Cong & Zhang, Jie & Zhang, Wenqi & Hodge, Bri-Mathias, 2022. "Convolutional neural networks for intra-hour solar forecasting based on sky image sequences," Applied Energy, Elsevier, vol. 310(C).
    9. Ahmed Aljanad & Nadia M. L. Tan & Vassilios G. Agelidis & Hussain Shareef, 2021. "Neural Network Approach for Global Solar Irradiance Prediction at Extremely Short-Time-Intervals Using Particle Swarm Optimization Algorithm," Energies, MDPI, vol. 14(4), pages 1-20, February.
    10. Gao, Bixuan & Huang, Xiaoqiao & Shi, Junsheng & Tai, Yonghang & Zhang, Jun, 2020. "Hourly forecasting of solar irradiance based on CEEMDAN and multi-strategy CNN-LSTM neural networks," Renewable Energy, Elsevier, vol. 162(C), pages 1665-1683.
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    3. Zang, Haixiang & Chen, Dianhao & Liu, Jingxuan & Cheng, Lilin & Sun, Guoqiang & Wei, Zhinong, 2024. "Improving ultra-short-term photovoltaic power forecasting using a novel sky-image-based framework considering spatial-temporal feature interaction," Energy, Elsevier, vol. 293(C).
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    5. Mirza, Adeel Feroz & Mansoor, Majad & Usman, Muhammad & Ling, Qiang, 2023. "A comprehensive approach for PV wind forecasting by using a hyperparameter tuned GCVCNN-MRNN deep learning model," Energy, Elsevier, vol. 283(C).

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