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A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea

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  • Nam, KiJeon
  • Hwangbo, Soonho
  • Yoo, ChangKyoo

Abstract

Renewable and sustainable energy systems and policies have globally been promoted to transition from fossil fuel sources to environmentally friendly renewable energy sources such as wind power, photovoltaic energy, and fuel cells. Wind and solar energy sources are erratic and difficult to implement in renewable energy systems, therefore, circumspection is needed to implement such renewable energy systems and policies. Accordingly, this study develops an energy forecasting model with renewable energy technologies on which policy can be based, using the Korean energy policy as a case study. Deep learning-based models forecast fluctuating variation in electricity demand and generation, which are necessary in renewable energy system but not possible with conventional models. The gated recurrent unit shows the best prediction performance among the forecasting models evaluated, and is therefore selected as the base model to evaluate four different renewable energy scenarios. The scenarios are evaluated according to economic-environmental cost assessment. The optimal scenario uses an integrated gasification combined cycle, onshore and offshore wind farms, photovoltaic power stations, and fuel cell plants; in particular, this scenario shows the lowest economic-environmental costs, generates stable electricity for demand, and achieves a policy with 100% renewable energy. The optimal scenario is assessed by considering its strengths, weaknesses, opportunities, and threats analysis while also considering techno-economic-environmental domestic and global energy circumstances.

Suggested Citation

  • Nam, KiJeon & Hwangbo, Soonho & Yoo, ChangKyoo, 2020. "A deep learning-based forecasting model for renewable energy scenarios to guide sustainable energy policy: A case study of Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 122(C).
    • Handle: RePEc:eee:rensus:v:122:y:2020:i:c:s1364032120300228
    DOI: 10.1016/j.rser.2020.109725
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