New & Renewable Energy (신재생에너지)

Korean Society for New and Renewable Energy (한국신·재생에너지학회)
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CNN-LSTM based Wind Power Prediction System to Improve Accuracy

정확도 향상을 위한 CNN-LSTM 기반 풍력발전 예측 시스템

  • Park, Rae-Jin (Department of Electrical Engineering, Hanbat National University) ;
  • Kang, Sungwoo (Department of Electrical Engineering, Korea University) ;
  • Lee, Jaehyeong (Korea Electric Power Corporation) ;
  • Jung, Seungmin (Department of Electrical Engineering, Hanbat National University)
  • Received : 2022.01.05
  • Accepted : 2022.03.24
  • Published : 2022.06.25
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Abstract

In this study, we propose a wind power generation prediction system that applies machine learning and data mining to predict wind power generation. This system increases the utilization rate of new and renewable energy sources. For time-series data, the data set was established by measuring wind speed, wind generation, and environmental factors influencing the wind speed. The data set was pre-processed so that it could be applied appropriately to the model. The prediction system applied the CNN (Convolutional Neural Network) to the data mining process and then used the LSTM (Long Short-Term Memory) to learn and make predictions. The preciseness of the proposed system is verified by comparing the prediction data with the actual data, according to the presence or absence of data mining in the model of the prediction system.

Keywords

Acknowledgement

This work was supported by the Korea Electric Power Corporation grant (No. R21XO01-23) and the Korea Institute of Energy Technology Evaluation and Planning (KETEP) grant funded by the Korea government (MOTIE) (No. 20183010025440).

References

  1. IRENA, "Wind Energy", https://www.irena.org/wind.
  2. Byon, J.Y., Kim, Y.M., and Choi, B.C., 2014, "Variability of future wind and solar resource over the korean peninsula based on climate change scenario", New. Renew. Energy, 10(2), 29-39. https://doi.org/10.7849/ksnre.2014.10.2.029
  3. Nam, I.S., Lee, J.W., and Jung, S.M., 2021, "A study about the curtailment for wind turbines considering power sensitivity of distribution system", The Transactions of the Korean Institute of Electrical Engineers, 71(3), 495-502.
  4. Myung, H.S., Kim, H.C., Kang, N.H., Kim, Y.H., and Kim, S.H., 2018, "Analysis of the load contribution of wind power and photovoltaic power to power system in Jeju", J. Korean Sol. Energy Soc., 38(1), 13-24. https://doi.org/10.7836/kses.2018.38.1.013
  5. Jin, S.J., and Cho, H.J., 2020, "Seasonal time series forecasting using machine learning", JKDAS, 22(5), 1779-1791.
  6. Li, Z., Zou, H., and Qi, B., 2019, "Application of ARIMA and LSTM in relative humidity prediction", IEEE 19th International Conference on Communication Technology (ICCT), 1544-1549.
  7. An, Y.J., Lee, T.K., and Kim, K.H., 2021, "Prediction of photovoltaic power generation based on LSTM considering daylight and solar radiation data", Trans. Korean. Inst. Elect. Eng., 70(8), 1096-1101. https://doi.org/10.5370/KIEE.2021.70.8.1096
  8. Kim, D.J., Hwang, C.G., and Yoon, C.P., 2020, "Learning data preprocessing technique for improving indoor positioning performance based on machine learning,", JKIICE, 24(11), 1528-1533.
  9. Korea Offshore Wind Power, http://www.kowp.co.kr/data/file.asp?menu=14.
  10. Zhao, B., Lu, H., Chen, S., Liu, J., and Wu, D., 2017, "Convolutional neural networks for time series classification", Journal of Systems Engineering and Electronics, 28(1), 162-169. https://doi.org/10.21629/jsee.2017.01.18
  11. Kim, T.Y., and Cho, S.B., 2019, "Predicting residential energy consumption using CNN-LSTM neural networks", Energy, 182, 72-81. https://doi.org/10.1016/j.energy.2019.05.230