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Comparative Analysis of Solar Power Generation Prediction AI Model DNN-RNN

  • Journal of Internet of Things and Convergence
  • Abbr : JKIOTS
  • 2022, 8(3), pp.55-61
  • Publisher : The Korea Internet of Things Society
  • Research Area : Engineering > Computer Science > Internet Information Processing
  • Received : April 11, 2022
  • Accepted : May 28, 2022
  • Published : June 30, 2022

Jeong-Jo Hong 1 Yong sun Oh 1

1목원대학교

Accredited

ABSTRACT

In order to reduce greenhouse gases, the main culprit of global warming, the United Nations signed the Climate Change Convention in 1992. Korea is also pursuing a policy to expand the supply of renewable energy to reduce greenhouse gas emissions. The expansion of renewable energy development using solar power led to the expansion of wind power and solar power generation. The expansion of renewable energy development, which is greatly affected by weather conditions, is creating difficulties in managing the supply and demand of the power system. To solve this problem, the power brokerage market was introduced. Therefore, in order to participate in the power brokerage market, it is necessary to predict the amount of power generation. In this paper, the prediction system was used to analyze the Yonchuk solar power plant. As a result of applying solar insolation from on-site (Model 1) and the Korea Meteorological Administration (Model 2), it was confirmed that accuracy of Model 2 was 3% higher. As a result of comparative analysis of the DNN and RNN models, it was confirmed that the prediction accuracy of the DNN model improved by 1.72%.

Citation status

* References for papers published after 2022 are currently being built.