정보처리학회논문지:소프트웨어 및 데이터공학 (KIPS Transactions on Software and Data Engineering)

  • 제11권7호
  • /
  • Pages.307-314
  • /
  • 2022
  • /
  • 2287-5905(pISSN)
  • /
  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
권호 표지
DOI QR코드

DOI QR Code

기온 데이터를 반영한 전력수요 예측 딥러닝 모델

Electric Power Demand Prediction Using Deep Learning Model with Temperature Data

  • 윤협상 (대구가톨릭대학교 컴퓨터정보학부) ;
  • 정석봉 (경일대학교 철도학부)
  • 투고 : 2022.03.17
  • 심사 : 2022.05.04
  • 발행 : 2022.07.31
PDF 다운로드
⟨ 이전 논문 다음 논문 ⟩

초록

최근 전력수요를 예측하기 위해 통계기반 시계열 분석 기법을 대체하기 위해 딥러닝 기법을 활용한 연구가 활발히 진행되고 있다. 딥러닝 기반 전력수요 예측 연구 결과를 분석한 결과, LSTM 기반 예측 모델의 성능이 우수한 것으로 규명되었으나 장기간의 지역 범위 전력수요 예측에 대해 LSTM 기반 모델의 성능이 충분하지 않음을 확인할 수 있다. 본 연구에서는 기온 데이터를 반영하여 24시간 이전에 전력수요를 예측하는 WaveNet 기반 딥러닝 모델을 개발하여, 실제 사용하고 있는 통계적 시계열 예측 기법의 정확도(MAPE 값 2%)보다 우수한 예측 성능을 달성하는 모델을 개발하고자 한다. 먼저 WaveNet의 핵심 구조인 팽창인과 1차원 합성곱 신경망 구조를 소개하고, 전력수요와 기온 데이터를 입력값으로 모델에 주입하기 위한 데이터 전처리 과정을 제시한다. 다음으로, 개선된 WaveNet 모델을 학습하고 검증하는 방법을 제시한다. 성능 비교 결과, WaveNet 기반 모델에 기온 데이터를 반영한 방법은 전체 검증데이터에 대해 MAPE 값 1.33%를 달성하였고, 동일한 구조의 모델에서 기온 데이터를 반영하지 않는 것(MAPE 값 2.31%)보다 우수한 전력수요 예측 결과를 나타내고 있음을 확인할 수 있다.

Recently, researches using deep learning-based models are being actively conducted to replace statistical-based time series forecast techniques to predict electric power demand. The result of analyzing the researches shows that the performance of the LSTM-based prediction model is acceptable, but it is not sufficient for long-term regional-wide power demand prediction. In this paper, we propose a WaveNet deep learning model to predict electric power demand 24-hour-ahead with temperature data in order to achieve the prediction accuracy better than MAPE value of 2% which statistical-based time series forecast techniques can present. First of all, we illustrate a delated causal one-dimensional convolutional neural network architecture of WaveNet and the preprocessing mechanism of the input data of electric power demand and temperature. Second, we present the training process and walk forward validation with the modified WaveNet. The performance comparison results show that the prediction model with temperature data achieves MAPE value of 1.33%, which is better than MAPE Value (2.33%) of the same model without temperature data.

키워드

과제정보

이 논문은 2022년도 대구가톨릭대학교 교내연구비 지원에 의한 것임.

참고문헌

  1. G. Kim, G. Lee, I. Choi, and J. Kim, "Forecasting strategy for hydropower power market price by power demand analysis and forecast," Proceedings of the KIEE Conference, pp.656-657, 2011.
  2. K. H. Kim, B. Chang, and H. K. Choi, "Deep learning based short-term electric load forecasting models using one-hot encoding," Journal of IKEEE, Vol.23, No.3, pp.852-857, 2019. https://doi.org/10.7471/IKEEE.2019.23.3.852
  3. T. Hong, M. Gui, M. Baran, and H. L. Willis, "Modeling and forecasting hourly electric load by multiple linear regression with interactions," Proceedings of the IEEE Power and Energy Society General Meeting, pp.1-8, 2010.
  4. T. Hong, P. Wang, and H. L. Willis, "A naive multiple linear regression benchmark for short term load forecasting," Proceedings of the IEEE Power and Energy Society General Meeting, Vol.2, pp.1-6, 2011.
  5. A. Karim and S. Ariffin, "Electricity load forecasting in UTP using moving averages and exponential smoothing techniques," Applied Mathematical Sciences, Vol.7, No.77-80, pp.4003-4014, 2013. https://doi.org/10.12988/ams.2013.33149
  6. S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, Vol.9, No.8, pp.1735-1780, 1997 https://doi.org/10.1162/neco.1997.9.8.1735
  7. K. Cho, B. van Merrienboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, "Learning phrase representations using RNN encoder-decoder for statistical machine translation," Proceedings of Empirical Methods in Natural Language Processing, pp.1724-1734, 2014.
  8. D. Lee, Y. Sun, S. Kim, I. Sim, Y. Hwang, and J. Kim, "Comparison of power consumption prediction scheme based on artificial intelligence," The Journal of the Institute of Internet, Broadcasting and Communication, Vol.19, No.4, pp.161-167, 2019.
  9. J. Lee and D. Kim, "A study on short-term electricity demand prediction using stacking ensemble of machine learning and deep learning ensemble models," Proceedings of ACK 2021, Vol.28, No.2, pp.566-569, 2021.
  10. S. Lee, J. Seon, S. Kim, and J. Kim, "Power trading system through the prediction of demand and supply in distributed power system based on deep," Journal of the Institute of Internet, Broadcasting and Communication, Vol.21, No.6, pp.163-171, 2021. https://doi.org/10.7236/JIIBC.2021.21.6.163
  11. J. Park, D. Shin, C. Kim, C. Author, and C. Kim, "Deep learning model for electric power demand prediction using special day separation and prediction elements extention," Journal of Advanced Navigation Technology, Vol.21, No.4, pp.365-370, 2017. https://doi.org/10.12673/JANT.2017.21.4.365
  12. J. Choi, "Performance comparison of machine learning in the prediction for amount of power market," Journal of the Korea Institute of Electronic Communication Sciences, Vol.14, No.5, pp.943-950, 2019. https://doi.org/10.13067/JKIECS.2019.14.5.943
  13. H. Tak, T. Kim, H.-G. Cho, and H. Kim, "A new prediction model for power consumption with local weather information," Journal of the Korea Institute of Electronic Communication Sciences, Vol.16, No.11, pp.488-498, 2016.
  14. A. van den Oord, et al., "WaveNet: A generative model for raw audio," arXiv preprint arXiv:1609.03499, 2016.
  15. H.-S. Yoon, "Time series data analysis using wavenet and walk forward validation," Journal of the Korea Society for Simulation, Vol.30, No.4, pp.1-8, 2021. https://doi.org/10.9709/JKSS.2021.30.4.001
  16. I. E. Livieris, P. Emmanuel, and P. Panagiotis, "A CNN-LSTM model for gold price time-series forecasting," Neural Computing and Applications, Vol.32, No.23, pp.17351-17360, 2020. https://doi.org/10.1007/s00521-020-04867-x
  17. A. Tealab, "Time series forecasting using artificial neural networks methodologies: A systematic review," Future Computing and Informatics Journal, Vol.3, No.2, pp.334-340. 2018. https://doi.org/10.1016/j.fcij.2018.10.003
  18. J. Moon, S. Park, and E. Hwang, "A multilayer perceptron-based electric load forecasting scheme via effective recovering missing data," KIPS Transactions on Software and Data Engineering, Vol.8, No.2, pp.67-87, 2019. https://doi.org/10.3745/KTSDE.2019.8.2.67
  19. KMA Weather Data Service, [Internet], https://data.kma.go.kr/.
  20. T. T. Ngoc, L. van Dai, and D. T. Phuc, "Grid search of multilayer perceptron based on the walk-forward validation methodology," International Journal of Electrical and Computer Engineering, Vol.11, No.2, pp.1742-1751, 2021.
  21. J. Bergstra, R. Bardenet, Y. Bengio, and B. Kegl, "Algorithms for hyper-parameter optimization," Proceedings of 25th Annual Conference on Neural Information Processing Systems, pp.1-9, 2011.
  22. I. Babuschkin, "A TensorFlow implementation of DeepMind's WaveNet paper," Github, 2022, [Internet], https://github.com/ibab/tensorflow-wavenet.
  23. D. Masters and C. Luschi, "Revisiting small batch training for deep neural networks," pp.1-18, arXiv preprint arXiv:1804.07612, 2018.