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Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference

Author

Listed:
  • Jeseok Ryu

    (Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea)

  • Jinho Kim

    (Gwangju Institute of Science and Technology (GIST), Gwangju 61005, Korea)

Abstract

The increase in ambient particulate matter (PM) is affecting not only our daily life but also various industries. To cope with the issue of PM, which has been detrimental to the population of megacities, an advanced demand response (DR) program is established by Korea Power Exchange (KPX) to supplement existing policies in Korea. Ironically, however, DR programs have been launched hurriedly, creating problems for several stakeholders such as local governments, market operators, and DR customers. As an alternative, a method for predicting and categorizing the PM through deep learning and fuzzy inference is suggested in this study. The simulation results based on Seoul data show that the proposed model can overcome the problems related to current DR programs and policy loopholes and can provide improvements for some stakeholders. However, the proposed model also has some limitations, which require an in-depth policy consideration or an incentive system for power generation companies.

Suggested Citation

  • Jeseok Ryu & Jinho Kim, 2020. "Demand Response Program Expansion in Korea through Particulate Matter Forecasting Based on Deep Learning and Fuzzy Inference," Energies, MDPI, vol. 13(23), pages 1-14, December.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:23:p:6393-:d:455681
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    References listed on IDEAS

    as
    1. Jeseok Ryu & Jinho Kim, 2020. "Non-Cooperative Indirect Energy Trading with Energy Storage Systems for Mitigation of Demand Response Participation Uncertainty," Energies, MDPI, vol. 13(4), pages 1-14, February.
    2. Sulandari, Winita & Subanar, & Lee, Muhammad Hisyam & Rodrigues, Paulo Canas, 2020. "Indonesian electricity load forecasting using singular spectrum analysis, fuzzy systems and neural networks," Energy, Elsevier, vol. 190(C).
    3. Jiyong Eom & Minwoo Hyun & Jaewoong Lee & Hyoseop Lee, 2020. "Increase in household energy consumption due to ambient air pollution," Nature Energy, Nature, vol. 5(12), pages 976-984, December.
    4. Zhe Liu & Xueli Chen & Jinyang Cai & Tomas Baležentis & Yue Li, 2020. "The Impact of “Coal to Gas” Policy on Air Quality: Evidence from Beijing, China," Energies, MDPI, vol. 13(15), pages 1-11, July.
    5. Minyoung Roh & Seungho Jeon & Soontae Kim & Sha Yu & Almas Heshmati & Suduk Kim, 2020. "Modeling Air Pollutant Emissions in the Provincial Level Road Transportation Sector in Korea: A Case Study of the Zero-Emission Vehicle Subsidy," Energies, MDPI, vol. 13(15), pages 1-22, August.
    6. Constantin C. Bungău & Ioana Francesca Prada & Marcela Prada & Constantin Bungău, 2019. "Design and Operation of Constructions: A Healthy Living Environment-Parametric Studies and New Solutions," Sustainability, MDPI, vol. 11(23), pages 1-19, December.
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