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Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting

Author

Listed:
  • Yongju Son

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Yeunggurl Yoon

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

  • Jintae Cho

    (Korea Electric Power Research Institute, Daejeon 34056, Korea)

  • Sungyun Choi

    (School of Electrical Engineering, Korea University, Seoul 02841, Korea)

Abstract

Photovoltaic power generation must be predicted to counter the system instability caused by an increasing number of photovoltaic power-plant connections. In this study, a method for predicting the cloud volume and power generation using satellite images is proposed. Generally, solar irradiance and cloud cover have a high correlation. However, because the predicted solar irradiance is not provided by the Meteorological Administration or a weather site, cloud cover can be used instead of the predicted solar radiation. A lot of information, such as the direction and speed of movement of the cloud is contained in the satellite image. Therefore, the spatio-temporal correlation of the cloud is obtained from satellite images, and this correlation is presented pictorially. When the learning is complete, the current satellite image can be entered at the current time and the cloud value for the desired time can be obtained. In the case of the predictive model, the artificial neural network (ANN) model with the identical hyperparameters or setting values is used for data performance evaluation. Four cases of forecasting models are tested: cloud cover, visible image, infrared image, and a combination of the three variables. According to the result, the multivariable case showed the best performance for all test periods. Among single variable models, cloud cover presented a fair performance for short-term forecasting, and visible image presented a good performance for ultra-short-term forecasting.

Suggested Citation

  • Yongju Son & Yeunggurl Yoon & Jintae Cho & Sungyun Choi, 2022. "Cloud Cover Forecast Based on Correlation Analysis on Satellite Images for Short-Term Photovoltaic Power Forecasting," Sustainability, MDPI, vol. 14(8), pages 1-24, April.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:8:p:4427-:d:789392
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    References listed on IDEAS

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    Cited by:

    1. Qingyuan Wang & Longnv Huang & Jiehui Huang & Qiaoan Liu & Limin Chen & Yin Liang & Peter X. Liu & Chunquan Li, 2022. "A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    2. Jinhyeok Park & Young Jae Lee & Yongwon Jo & Jaehoon Kim & Jin Hyun Han & Kuk Jin Kim & Young Taeg Kim & Seoung Bum Kim, 2022. "Spatio-Temporal Network for Sea Fog Forecasting," Sustainability, MDPI, vol. 14(23), pages 1-10, December.

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