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A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant

Author

Listed:
  • Jeehong Kim

    (Department of Renewable Energy Engineering, Jeonju Vision College of University, Jeonju 55069, Republic of Korea)

  • Seok-ho Lee

    (Department of Renewable Energy Engineering, Jeonju Vision College of University, Jeonju 55069, Republic of Korea)

  • Kil To Chong

    (Department of Electronics and Information Engineering, Jeonbuk National University, Jeonju 54896, Republic of Korea
    Advances Electronics and Information Research Center, Jeonbuk National University, Jeonju 54896, Republic of Korea)

Abstract

In the process of creating a prediction model using artificial intelligence by utilizing a deep neural network, it is of utmost significance to know the amount of insolation that has an absolute effect on the quantity of power generation of a solar cell. To predict the power generation quantity of a solar power plant, a deep neural network requires previously accumulated power generation data of a power plant. However, if there is no equipment to measure solar radiation in the internal facilities of the power plant and if there is no record of the existence of solar radiation in the past data, it is inevitable to obtain the solar radiation information of the nearest point in an effort to accurately predict the quantity of power generation. The site conditions of the power plant are affected by the geographical topography which acts as a stumbling block while anticipating favorable weather conditions. In this paper, we introduce a method to solve these problems and predict the quantity of power generation by modeling the power generation characteristics of a power plant using a neural network. he average of the error between the actual quantity and the predicted quantity for the same period was 1.99, that represents the predictive model is efficient to be used in real-time.

Suggested Citation

  • Jeehong Kim & Seok-ho Lee & Kil To Chong, 2022. "A Study of Neural Network Framework for Power Generation Prediction of a Solar Power Plant," Energies, MDPI, vol. 15(22), pages 1-19, November.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:22:p:8582-:d:974802
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    References listed on IDEAS

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    1. Wen-Chang Tsai & Chia-Sheng Tu & Chih-Ming Hong & Whei-Min Lin, 2023. "A Review of State-of-the-Art and Short-Term Forecasting Models for Solar PV Power Generation," Energies, MDPI, vol. 16(14), pages 1-30, July.

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