DOI QR코드

DOI QR Code

유사 시계열 데이터 분석에 기반을 둔 교육기관의 전력 사용량 예측 기법

Power Consumption Forecasting Scheme for Educational Institutions Based on Analysis of Similar Time Series Data

  • 문지훈 (고려대학교 전기전자공학과) ;
  • 박진웅 (고려대학교 전기전자공학과) ;
  • 한상훈 (고려대학교 전기전자공학과) ;
  • 황인준 (고려대학교 전기전자공학과)
  • 투고 : 2017.01.26
  • 심사 : 2017.06.10
  • 발행 : 2017.09.15

초록

안정적인 전력 공급은 전력 인프라의 유지 보수 및 작동에 매우 중요하며, 이를 위해 정확한 전력 사용량 예측이 요구된다. 대학 캠퍼스는 전력 사용량이 많은 곳이며, 시간과 환경에 따른 전력 사용량 변화폭이 다양하다. 이러한 이유로, 전력계통의 효율적인 운영을 위해서는 전력 사용량을 정확하게 예측할 수 있는 모델이 요구된다. 기존의 시계열 예측 기법은 학습 시점과 예측 시점 간의 차이가 클수록 예측 구간이 넓어짐으로 예측 성능이 크게 떨어진다는 단점이 있다. 본 논문은 이를 보완하려는 방안으로, 먼저 의사결정나무를 이용해 날짜, 요일, 공휴일 여부, 학기 등을 고려하여 시계열 형태가 유사한 전력 데이터를 분류한다. 다음으로 분류된 데이터 셋에 각각의 자기회귀누적이동평균모형을 구성하여, 예측 시점에서 시계열 교차검증을 적용해 대학 캠퍼스의 일간 전력 사용량 예측 기법을 제안한다. 예측의 정확성을 평가하기 위해, 성능 평가 지표를 이용하여 제안한 기법의 타당성을 검증하였다.

A stable power supply is very important for the maintenance and operation of the power infrastructure. Accurate power consumption prediction is therefore needed. In particular, a university campus is an institution with one of the highest power consumptions and tends to have a wide variation of electrical load depending on time and environment. For this reason, a model that can accurately predict power consumption is required for the effective operation of the power system. The disadvantage of the existing time series prediction technique is that the prediction performance is greatly degraded because the width of the prediction interval increases as the difference between the learning time and the prediction time increases. In this paper, we first classify power data with similar time series patterns considering the date, day of the week, holiday, and semester. Next, each ARIMA model is constructed based on the classified data set and a daily power consumption forecasting method of the university campus is proposed through the time series cross-validation of the predicted time. In order to evaluate the accuracy of the prediction, we confirmed the validity of the proposed method by applying performance indicators.

키워드

과제정보

연구 과제 주관 기관 : 한국에너지기술평가원(KETEP)

참고문헌

  1. A. I. Saleh, A. H. Rabie, and K. M. Abo-Al-Ez, "A data mining based load forecasting strategy for smart electrical grids," Advanced Engineering Informatics, Vol. 30, No. 3, pp. 422-448, Aug. 2016. https://doi.org/10.1016/j.aei.2016.05.005
  2. M. Q. Raza and A. Khosravi, "A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings," Renewable and Sustainable Energy Reviews, Vol. 50, pp. 1352-1372, Oct. 2015. https://doi.org/10.1016/j.rser.2015.04.065
  3. L. Hernandez, C. Baladron, J. M. Aguiar, B. Carro, A. J. Sanchez-Esguevillas, J. Lloret, and J. Massana, "A survey on electric power demand forecasting: future trends in smart grids, microgrids and smart buildings," IEEE Communications Surveys & Tutorials, Vol. 16, No. 3, pp. 1460-1495, Apr. 2014. https://doi.org/10.1109/SURV.2014.032014.00094
  4. S. Y. Son, S. H. Lee, K. Chung, and J. S. Lim, "Feature selection for daily peak load forecasting using a neuro-fuzzy system," Multimedia Tools and Applications, Vol. 74, No. 7, pp. 2321-2336, Apr. 2015. https://doi.org/10.1007/s11042-014-1943-0
  5. T. Hong and S. Fan, "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Vol. 32, No. 3, pp. 914-938, Jul.-Sep. 2016. https://doi.org/10.1016/j.ijforecast.2015.11.011
  6. J. Moon, J. Park, E. Hwang, and S. Jun, "Forecasting power consumption for higher educational institutions based on machine learning," The Journal of Supercomputing, pp. 1-23, 2017.
  7. J. Moon, S. Jun, J. Park, Y. Choi, and E. Hwang, "An Electric Load Forecasting Scheme for University Campus Buildings Using Artificial Neural Network and Support Vector Regression," KIPS Transactions on Computer and Communication Systems, Vol. 5, No. 10, pp. 293-302, Oct. 2016. (in Korean) https://doi.org/10.3745/KTCCS.2016.5.10.293
  8. M. H. Chung and E. K. Rhee, "Potential opportunities for energy conservation in existing buildings on university campus: A field survey in Korea," Energy and Buildings, Vol. 78, pp. 176-182, Aug. 2014. https://doi.org/10.1016/j.enbuild.2014.04.018
  9. W. Lee, D. Lee, J. Lee, J. Yoon, and U. Shin, "A Case Study of Electric Power Consumption Characteristics in University Building," Journal of the Korean Solar Energy Society, Vol. 32, No. 4, pp. 90-95. Aug. 2012. (in Korean) https://doi.org/10.7836/kses.2012.32.4.090
  10. B. Koo, W. Hong, and K. Kim, "A Study on the Energy Reduction Effect Using Renewable Energy Through the Analysis of Energy Consumption Structure in the University Buildings," Journal of the Architectural Institute of Korea Planning & Design, Vol. 29, No. 9, pp. 203-210, Sep, 2013. (in Korean) https://doi.org/10.5659/JAIK_PD.2013.29.9.203
  11. N. S. Youn and J. T. Kim, "Survey and Analysis of Power Energy Usage of University Buildings," Journal of the Korea Institute of Ecological Architecture and Environment, Vol. 13, No. 2, pp. 27-32, Apr. 2013. (in Korean)
  12. J. Jung, D. Kim, J. Lee, J. Yang, and H. Seok, "The Survey and Analysis of Electric Power Consumption in University Building by Analyzing Case Study," Journal of The Korean Society of Living Environmental System, Vol. 17, No. 1, pp. 1-9, Feb. 2010. (in Korean)
  13. J. Moon, E. Ha, J. Park, and E. Hwang, "Daily Electric Load Forecasting Scheme for Educational Institution Based on Decision Tree and ARIMA Model," Proc. of the 43th KIISE Winter Conference, pp. 6-8, 2016. (in Korean)
  14. H. Yeon and Y. Jang, "Visual Analytics for Abnormal Event detection using Seasonal-Trend Decomposition and Serial-Correlation," Journal of KIISE, Vol. 41, No. 12, pp. 1066-1074, Dec. 2014. (in Korean) https://doi.org/10.5626/JOK.2014.41.12.1066
  15. J. Kim, C. Lee, and K. Shim, "Time Series Prediction using Clustering Algorithm," Journal of KIISE : Computing Practices and Letters, Vol. 20, No. 3, pp. 191-195, Mar. 2014. (in Korean)
  16. W. Lee, S. Yoon, and S. Lee, "Evaluation of Structural Changes of a Controlled Group Using Time-Sequential SNA," Journal of KIISE, Vol. 43, No. 10, pp. 1124-1130, Oct. 2016. (in Korean) https://doi.org/10.5626/JOK.2016.43.10.1124
  17. B. Moon and M. Choi, "Compression Methods for Time Series Data using Discrete Cosine Transform with Varying Sample Size," KIISE Transactions on Computing Practices, Vol. 22, No. 5, pp. 201-208, May. 2016. (in Korean) https://doi.org/10.5626/KTCP.2016.22.5.201
  18. S. Hong, Y. Moon, and H. Kim, "Privacy-Preserving Time-Series Data Mining," Journal of KISS : Databases, Vol. 40, No. 2, pp. 124-133, Apr. 2013. (in Korean)
  19. L. Rutkowski, M. Jaworski, L. Pietruczuk, and P. Duda, "The CART decision tree for mining data streams," Information Sciences, Vol. 266, pp. 1-15, May 2014. https://doi.org/10.1016/j.ins.2013.12.060
  20. R. J. Hyndman and G. Athanasopoulos, Forecasting: principles and practice, OTexts, 2014.
  21. H. Akaike, "Akaike's Information Criterion," International Encyclopedia of Statistical Science, Springer Berlin Heidelberg, pp. 25-25, Dec. 2011.
  22. A. M. De Livera, R. J. Hyndman, and R. D. Snyder, "Forecasting Time Series With Complex Seasonal Patterns Using Exponential Smoothing," Journal of the American Statistical Association, Vol. 106, No. 496, pp. 1513-1527, Jan. 2012. https://doi.org/10.1198/jasa.2011.tm09771