The Journal of the Korea institute of electronic communication sciences (한국전자통신학회논문지)

Korea Institute of Electronic Communication Science (한국전자통신학회)
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Prediction of Wind Power Generation for Calculation of ESS Capacity using Multi-Layer Perceptron

ESS 용량 산정을 위한 다층 퍼셉트론을 이용한 풍력 발전량 예측

  • Received : 2021.02.26
  • Accepted : 2021.04.17
  • Published : 2021.04.30
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Abstract

In this paper, we perform prediction of amount of electric power plant for complex of wind plant using multi-layer perceptron in order to calculate exact calculation of capacity of ESS to maximize profit through generation and to minimize generation cost of wind generation. We acquire wind speed, direction of wind and air density as variables to predict the amount of generation of wind power. Then, we merge and normalize there variables. To train model, we divide merged variables into data as train and test data with ratio of 70% versus 30%. Then we train model by using training data, and we alsouate the prediction performance of model by using test data. Finally, we present the result of prediction in amount of wind power.

본 논문에서는 풍력 발전 수익 극대화 및 비용 최소화를 위해 설치하는 ESS에 대하여 정확한 용량 산정을 하기 위한 목적으로 풍력 단지용 전력량 예측을 다층 퍼셉트론을 이용하여 수행한다. 풍력 발전량을 예측하기 위해 풍속, 풍향, 공기밀도를 변수로 하고 그 변수를 병합하고 정규화한다. 모델을 훈련시키기 위해 병합된 변수를 70% 대 30% 비율로 훈련 및 테스트 데이터로 나눈다. 그런 다음 학습 데이터를 사용하여 모델을 학습시키고 테스트 데이터를 사용하여 모델의 예측 성능도 평가한다. 마지막으로 풍력량 예측 결과를 제시한다.

Keywords

References

  1. Y. Lee, H. Kim, Y. Park, S. Park, J. Park, and Y. Kang, "Direction for the Mid- and Long-Term Development for Expanding Renewable Energy and Responding to Future Environmental Changes: Current Status and Direction of Onshore Wind Power," Korea environment institute report, 2020, pp. 1-101.
  2. Z. Dongmei, Z. Yuchen, and Z. Xu, "Research on wind power forecasting in wind farms," 2011 IEEE Power Engineering and Automation Conference, 2011.
  3. X. Wang, P. Guo, and X Huang, "A review of wind power forecasting models," Energy procedia 12, 2011, pp. 770-778. https://doi.org/10.1016/j.egypro.2011.10.103
  4. M. Lange and U. Focken, "New developments in wind energy forecasting," IEEE power and energy society general meeting-conversion and delivery of electrical energy in the 21st century, vol. 24, no. 3, 2008, pp. 1-8.
  5. G. Giebel, R. Brownsword, G. Kariniotakis, M. Denhard, and C. Draxl, "The state-of-the-art in short-term prediction of wind power: A literature overview," ANEMOS. plus, 2011.
  6. J. Hur, B. Park, and S. Jung, "The development of the wind power prediction system using ordinary kriging," J. Korean institute of illuminating and electrical installation engineers, vol. 30, no. 7, 2016, pp. 60-68. https://doi.org/10.5207/JIEIE.2016.30.7.060
  7. N. Son, Y. Kim, S. Kim, and D. Ahn, " Study of Multi-variate Short-term wind power forecasting model based on SVR," J. Korean institute of next generation computing, vol. 13, no. 1, 2017, pp. 54-64.
  8. J. Woo, H. Kim, B. Kim, I, Paek, and N. Yoo, "Prediction of annual energy production of Gangwon wind farm using AWS wind data," J. Korean solar energy society, vol. 31, no. 2, 2011, pp. 72-81. https://doi.org/10.7836/kses.2011.31.2.072
  9. N. Srivastava, G. Hinton, and A. Krizhevsky "Dropout: a simple way to prevent neural networks from overfitting," The Journal of Machine Learning Research, vol. 15, no. 1, 2014, pp. 1929-1958.
  10. M. Jeong, C. Moon, S. Kim, B. Park, and S. Chea, "Design of wind farm in South Western seaside area," The korean solar energy society(spring), vol. 22, no. 4, 2010, pp. 487-490.
  11. H. Byun, J. Ryu, and D. Kim, "The study of the wind resource and energy yield assessment for the wind park development," The Korean society for new and renewable energy, vol. 1, no. 2, 2005, pp. 19-25.
  12. S. Pal and S. Mitra, "Multilayer perceptron, fuzzy sets, classifiaction," Transactions on neural networks. IEEE, vol. 3, 1992, pp. 683-697. https://doi.org/10.1109/72.159058