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배전시스템 운영계획을 위한 신재생에너지원 발전량 예측 방법

Renewable Power Generation Forecasting Method for Distribution System: A Review

  • Cho, Jintae (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Kim, Hongjoo (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Ryu, Hosung (KEPCO Research Institute, Korea Electric Power Corporation) ;
  • Cho, Youngpyo (KEPCO Research Institute, Korea Electric Power Corporation)
  • 투고 : 2021.09.06
  • 심사 : 2022.04.28
  • 발행 : 2022.06.30

초록

Power generated from renewable energy has continuously increased recently. As the distributed generation begins to interconnect in the distribution system, an accurate generation forecasting has become important in efficient distribution planning. This paper explained method and current state of distributed power generation forecasting models. This paper presented selecting input and output variables for the forecasting model. In addition, this paper analyzed input variables and forecasting models that can use as mid-to long-term distributed power generation forecasting.

키워드

참고문헌

  1. Renewable Energy 3020 Implementation Plan, Ministry of Trade, Industry and Energy, 2017.
  2. Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M.Y.I., Deventer, W.V., Horan, B., Stojcevski, A., Forecasting of photovoltaic power generation and model optimization: A review. Renew. Sustain. Energy Rev., vol. 81, pp. 912-928, 2018. https://doi.org/10.1016/j.rser.2017.08.017
  3. W. Li, T. Logenthiran, W. L. Woo, "Multi-GRU prediction system for electricity generation's planning and operation," in IET Generation, Transmission & Distribution, vol. 13, no. 9, pp. 1630-1637, 2019. https://doi.org/10.1049/iet-gtd.2018.6081
  4. Akhter, M. N., Mekhilef, S., Mokhlis, H., & Shah, N. M., Review on forecasting of photovoltaic power generation based on machine learning and metaheuristic techniques, IET Renewable Power Generation, vol. 13, no. 7, pp. 1009-1023, 2019. https://doi.org/10.1049/iet-rpg.2018.5649
  5. Alsharif MH, Younes MK, Kim J., Time Series ARIMA Model for Prediction of Daily and Monthly Average Global Solar Radiation: The Case Study of Seoul, South Korea. Symmetry, vol. 11, pp. 240-257, 2019. https://doi.org/10.3390/sym11020240
  6. Sahm Kim, "A study on solar irradiance forecasting with weather variables", The Korean Journal of Applied Statistics, 2017.
  7. Oryani, B., Koo, Y., Rezania, S., "Structural Vector Autoregressive-Approach to Evaluate the Impact of Electricity Generation Mix on Economic Growth and CO2 Emissions in Iran," Energies, vol. 13, no. 4268, 2020.
  8. Y. Li et al., "An ARMAX model for forecasting the power output of a grid connected photovoltaic system," Renewable Energy, vol. 66, pp. 78-89, June 2014. https://doi.org/10.1016/j.renene.2013.11.067
  9. Jae Hyun Yoo, "Gaussian Process Model-based Reinforcement Learning," Journal of Institute of Control, Robotics and Systems, vol. 25, no. 8, pp. 746-751, 1997. https://doi.org/10.5302/j.icros.2019.18.0221
  10. A. G. Abo-Khalil and D. Lee, "SVR-based Wind Speed Estimation for Power Control of Wind Energy Generation System," 2007 Power Conversion Conference, Nagoya, pp. 1431-1436, 2007.
  11. J. Yan, K. Li, E. Bai and A. Foley, "Special condition wind power forecasting based on Gaussian Process and similar historical data," 2015 IEEE Power & Energy Society General Meeting, Denver, pp. 1-5, 2015.
  12. N. Chen, Z. Qian, I. T. Nabney and X. Meng, "Wind Power Forecasts Using Gaussian Processes and Numerical Weather Prediction," in IEEE Transactions on Power Systems, vol. 29, no. 2, pp. 656-665, March 2014. https://doi.org/10.1109/TPWRS.2013.2282366
  13. S. Bae, Research Trends of Photovoltaic Generation Forecasting, KIEE: World of electric, vol. 67, no. 12, pp. 16-25, 2018.
  14. A. Almadhor, "Performance prediction of distributed PV generation systems using Artificial Neural Networks (ANN) and Mesh Networks," International Conference on Smart Grid, Nagasaki, Japan, pp. 88-91, 2018.
  15. M. N. Rahman, A. Esmailpour, "An Efficient Electricity Generation Forecasting System Using Artificial Neural Network Approach with Big Data," IEEE First International Conference on Big Data Computing Service and Applications, Redwood City, CA, pp. 213-217, 2015.
  16. Gwon-Yoon Lee, Sang-Boo Lee, Universal Prediction SystemRealization Using RNN, Journal of KII, vol. 16, no. 10, pp. 11-20, 2018.
  17. Ospina, Juan, AlviNewaz, M. Omar Faruque, Forecasting of PV plant output using hybrid wavelet-based LSTM-DNN structure model, IET Renewable Power Generation, vol. 13, no. 7, pp. 1087-1095, 2019. https://doi.org/10.1049/iet-rpg.2018.5779
  18. Tomonobu, S., Atsushi, Y., Naomitsu, U., Toshihisa, F., "Application of Recurrent Neural Network to Long-Term-Ahead Generating Power Forecasting for Wind Power Generator," IEEE Power Systems Conference and Exposition, pp. 1260-1265, 2006.
  19. Jungin Lee, Il-Woo Lee. "Technology trends of Renewable energy generation forecasting based on ICT," The Journal of The Korean Institute of Communication Sciences, vol. 36, no. 11, pp. 3-8, 2019.
  20. K. W. Kim, G. S. Jang, S. M. Lim, I. K. Ahn, J. Park, H. C. Oh, "GRU-based Activity Recognition from Early-stage Motion," The Institute of Electronics and Information Engineers, pp. 2016-2019, 2020.
  21. Yong-jin Jung, Kyoung-woo Cho, Jong-sung Lee, Chang-heon Oh, Particulate Matter (PM10) Concentration Forecasting model using GRU, The Korea Institute of Information and Communication Engineering, vo. 23, no. 2, pp. 644-646, 2019.
  22. Ju-Hwan Ham, Sung-Yul Kim, "A Study on the Comparison of Kernel Functions Appropriate to the SVR-based Power Demand Prediction Algorithms," The Korean Institute of Electrical Engineers, Workshop, pp. 158-159, 2020.
  23. Mun, Gwon-Sun, Understanding of VAR model, KOSTAT, Research of statistics analysis, pp. 23-56, 1997.
  24. Boualit, S.B.; Mellit, A., "SARIMA-SVM hybrid model for the prediction of daily global solar radiation time series," In Proceedings of the 2016 International Renewable and Sustainable Energy Conference (IRSEC), IEEE, Marrakesh, Morocco, pp. 712-717, PP. 14-17, November 2016.