Comparison of Solar Power Prediction Model Based on Statistical and Artificial Intelligence Model and Analysis of Revenue for Forecasting Policy
Keywords:
Solar forecast, Deep learning, Electric brokerage market, Accuracy incentivesAbstract
Korea is pursuing a plan to switch and expand energy sources with a focus on renewable energy to become carbon neutral by 2050. As the instability of energy supply increases due to the intermittent nature of renewable energy, accurate prediction of the amount of renewable energy generation is becoming more important. Therefore, the government has opened a small-scale power brokerage market and is implementing a system that pays settlements according to the accuracy of renewable energy prediction. In this paper, a prediction model was implemented using a statistical model and an artificial intelligence model for the prediction of solar power generation. In addition, the results of prediction accuracy were compared and analyzed, and the revenue from the settlement amount of the renewable energy generation forecasting system was estimated.
References
The Presidential Commission on Carbon Neutrality and Green Growth. 2050 Carbon Neutrality of the Republic of Korea. https://www.2050cnc.go.kr
Joint Office of Korea Government, 2021, 2030 National Greenhouse Gas Reduction Target (NDC) Upgrade Plan, p. 5–6.
Korean Ministry of Commerce, Industry and Energy, 2017, Renewable Energy 3020 Implementation Plan, p. 2–10.
Kim Y-H, Myung H-S, Kang N-H, et al., 2018, Operation Plan of ESS for Increase of Acceptable Product of Renewable Energy to Power System. The Transactions of the Korean Institute of Electrical Engineers (KIEE), 67(11): 1401–1407. https://doi.org/10.5370/KIEE.2018.67.11. 1401
KEMRI. Renewable Intermittent Response Trend in Major Countries. http://www.keaj.kr
Korean Ministry of Commerce, Industry and Energy, 2020, Introduction of Renewable Energy Generation Prediction System.
KPX. Brokerage Market Operation Rules. https://der.kmos.kr/info/fr_notice_view.do
Korean Meteorological Agency. Short-Term Forecast. https://data.kma.go.kr
Gonzalez-Longatt F, Acosta MN, Chamorro HR, et al. 2020 International Conference on Smart Systems and Technologies, October 14–16, 2020: Short-Term Kinetic Energy Forecast Using a Structural Time Series Model: Study Case of Nordic Power System. 2020, Osijek, 173–178. https://doi.org/10.1109/SST49455.2020.9264087
Jeong H-Y, Hong S-H, Jeon J-S, et al., 2022, A Research of Prediction of Photovoltaic Power Using SARIMA Model. Journal of Korea Multimedia Society, 25(1): 82–91. https://doi.org/10.9717/kmms.2022.25.1.082
Min K-C, Ha H-K, 2014, Forecasting the Korea’s Port Container Volumes with SARIMA Model. Journal of Korean Society of Transportation, 32(6): 600–614. https://doi.org/ 10.7470/jkst.2014.32.6.600
Korea Meteorological Administration, 2020, [2020 Abnormal Climate Report].
