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LSTM–GAN based cloud movement prediction in satellite images for PV forecast

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Journal of Ambient Intelligence and Humanized Computing Aims and scope Submit manuscript

Abstract

Owing to the high uncertainty and variability of renewable energy, power system operators require an accurate forecast method. Considering that the cloud cover significantly affects the photovoltaic (PV) generation, critical factors for accurate PV forecast are the future shape and trajectory of clouds, which weather information services hardly provide. The paper proposes an innovative PV generation forecast method based on future cloud image prediction, for which a hybrid deep learning technique combining the generative adversarial network (GAN) and the long short-term memory (LSTM) model is used. The role of GAN is to generate cloud images from random latent vectors while LSTM learns patterns of time-series input images. To verify the effectiveness of the proposed methodology, the paper compares it with various hybrid PV forecast models in terms of prediction accuracy, using field data of satellite images and meteorological information. For testing the proposed method, a total of 30,507 infrared images shot by Communication, Ocean, and Meteorological Satellite 1 of the National Meteorological Satellite Center of Korea every 15 min were collected and utilized. In the end, it is concluded that the proposed LSTM–GAN model presents better prediction accuracy over CNN–ANN, CNN–LSTM, GRU–GAN, and BILSTM-GAN.

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Data Availability

The datasets generated during and/or analysed during the current study are available from the corresponding author on reasonable request.

Abbreviations

ANN:

Artificial neural network

ARIMAX:

Autoregressive integrated moving average exogenous

ARMA:

Autoregressive moving average

CNN:

Convolutional neural network

DCGAN:

Deep convolutional GAN

GAN:

Generative adversarial network

GHI:

Global horizontal irradiance

KMA:

Korea meteorological administration

LSTM:

Long short-term memory

MSE:

Mean squared error

PV:

Photovoltaic

RMSE:

Root mean squared error

SVM:

Support vector machine

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Funding

This research was supported in part by the Basic Research Program through the National Research Foundation of Korea (NRF) funded by the MSIT (No. 2020R1A4A1019405) and in part by Korea Electric Power Corporation (Grant number: R20XO02-19).

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Authors and Affiliations

  1. School of Electrical Engineering, Korea University, 145, Anam-ro, Seongbuk-gu, Seoul, 02841, South Korea

    Yongju Son, Xuehan Zhang, Yeunggurl Yoon & Sungyun Choi

  2. Korea Electric Power Research Institute, 105, Munji-ro, Yooseong-gu, Daejeon, 34056, South Korea

    Jintae Cho

Authors
  1. Yongju Son
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  2. Xuehan Zhang
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  3. Yeunggurl Yoon
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  4. Jintae Cho
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  5. Sungyun Choi
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Corresponding author

Correspondence to Sungyun Choi.

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Appendix

Appendix

Tables 2, 3, 4, 5, 6, 7, and 8 list the hyperparameters of DCGAN, CNN–ANN, CNN–LSTM, LTSM–GAN, GRU–GAN, BILSTM–GAN, and Multivariate-LSTM, respectively.

Table 2 GAN Hyperparameters
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Table 3 CNN-ANN Hyperparameters
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Table 4 CNN-LSTM Hyperparameters
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Table 5 LSTM-GAN Hyperparameters
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Table 6 GRU-GAN Hyperparameters
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Table 7 BILSTM-GAN Hyperparameters
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Table 8 Multivariate-LSTM Hyperparameters
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Son, Y., Zhang, X., Yoon, Y. et al. LSTM–GAN based cloud movement prediction in satellite images for PV forecast. J Ambient Intell Human Comput 14, 12373–12386 (2023). https://doi.org/10.1007/s12652-022-04333-7

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  • DOI: https://doi.org/10.1007/s12652-022-04333-7

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