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

  • 제6권11호
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  • Pages.527-536
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  • 2017
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  • 2287-5905(pISSN)
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  • 2734-0503(eISSN)
한국정보처리학회 (Korea Information Processing Society)
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인공 신경망 기반의 고시간 해상도를 갖는 전력수요 예측기법

An Electric Load Forecasting Scheme with High Time Resolution Based on Artificial Neural Network

  • 박진웅 (고려대학교 전기전자공학과) ;
  • 문지훈 (고려대학교 전기전자공학과) ;
  • 황인준 (고려대학교 전기전자공학과)
  • 투고 : 2017.07.12
  • 심사 : 2017.09.04
  • 발행 : 2017.11.30
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⟨ 이전 논문 다음 논문 ⟩

초록

최근 스마트 그리드 산업의 발달과 더불어 효과적인 에너지 관리 시스템의 필요성이 커지고 있다. 특히, 전기 부하 및 에너지 요금 감소를 위해서는 정확한 전력수요 예측과 그에 따른 효과적인 스마트 그리드 운영 전략이 필요하다. 본 논문에서는 보다 정확한 전력수요 예측을 위하여, 수요 시한 기준으로 수집된 전력 사용 데이터를 고시간 해상도로 분할하고, 이에 적합한 인공 신경망 기반의 전력수요 예측 모델을 구축하고자 한다. 예측 모델의 정확도를 향상시키기 위하여 우선, 수열 형태의 시계열 데이터가 가지는 주기성을 제대로 반영하지 못하는 기계 학습 모델의 문제점을 해결하고자, 시계열 데이터를 2차원 공간의 연속적인 데이터로 변환한다. 더욱이, 고시간 해상도에 따른 온도나 습도 등 외부 요인들의 보다 정확한 반영을 위해 이들에 대해서도 선형 보간법을 사용하여 세분화된 시점에서의 값을 추정하여 반영한다. 마지막으로, 구성된 특성 벡터에 대해 주성분 분석 수행을 통하여 불필요한 외부 요인을 제거한다. 예측 모델의 성능을 평가하기 위해서 5겹 교차 검증을 수행하였다. 실험 결과 모든 고시간 해상도에서 성능 향상을 보였으며, 특히 3분 해상도의 경우 3.71%의 가장 낮은 오차율을 보였다.

With the recent development of smart grid industry, the necessity for efficient EMS(Energy Management System) has been increased. In particular, in order to reduce electric load and energy cost, sophisticated electric load forecasting and efficient smart grid operation strategy are required. In this paper, for more accurate electric load forecasting, we extend the data collected at demand time into high time resolution and construct an artificial neural network-based forecasting model appropriate for the high time resolution data. Furthermore, to improve the accuracy of electric load forecasting, time series data of sequence form are transformed into continuous data of two-dimensional space to solve that problem that machine learning methods cannot reflect the periodicity of time series data. In addition, to consider external factors such as temperature and humidity in accordance with the time resolution, we estimate their value at the time resolution using linear interpolation method. Finally, we apply the PCA(Principal Component Analysis) algorithm to the feature vector composed of external factors to remove data which have little correlation with the power data. Finally, we perform the evaluation of our model through 5-fold cross-validation. The results show that forecasting based on higher time resolution improve the accuracy and the best error rate of 3.71% was achieved at the 3-min resolution.

키워드

참고문헌

  1. T. Hong and S. Fan, "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Vol.32, No.3, pp.914-938, 2016. https://doi.org/10.1016/j.ijforecast.2015.11.011
  2. A. Ahmad and T. Anderson, "Hourly power consumption prediction for New Zealand residential houses using artificial neural network models," in Proceedings of Asia-Pacific Solar Research Conference, Sydney, 2014.
  3. F. Rodrigues, C. Cardeira, and J.M.F. Calado, "The daily and hourly energy consumption and load forecasting using artificial neural network method: a case study using a set of 93 households in Portugal," Energy Procedia, Vol.62, pp.220-229, 2014. https://doi.org/10.1016/j.egypro.2014.12.383
  4. L. Hernandez, C. Baladron, J. M. Aguiar, B. Carro, A. J. Sanchez, and J. Lloret, "Short-term load forecasting for microgrids based on artificial neural networks," Energies, Vol.6, No.3, pp.1385-1408, 2013. https://doi.org/10.3390/en6031385
  5. A. Selakov, D. Cvijetinovic, L. Milovic, S. Mellon, and D. Bekut, "Hybrid PSO-SVM method for short-term load forecasting during periods with significant temperature variations in city of Burbank," Applied Soft Computing, Vol.16, pp.80-88, 2014. https://doi.org/10.1016/j.asoc.2013.12.001
  6. A. Vaghefi, M. Jafari, E. Bisse, Y. Lu, and J. Brouwer, "Modeling and forecasting of cooling and electricity load demand," Applied Energy, Vol.136, pp.186-196, 2014. https://doi.org/10.1016/j.apenergy.2014.09.004
  7. M. R. Sarkar, M. G. Rabbani, A. R. Khan, and M. M. Hossain, "Electricity demand forecasting of Rajshahi City in Bangladesh using fuzzy linear regression model," in Electrical Engineering and Information Communication Technology (ICEEICT), 2015.
  8. G. Dudek, "Pattern-based local linear regression models for short-term load forecasting," Electric Power Systems Research, Vol.130, pp.139-147, 2016. https://doi.org/10.1016/j.epsr.2015.09.001
  9. A.K. Fard and M.-R. Akbari-Zadeh, "A hybrid method based on wavelet, ANN and ARIMA model for short-term load forecasting," Journal of Experimental & Theoretical Artificial Intelligence, Vol.26, No.2, pp.167-182, 2014. https://doi.org/10.1080/0952813X.2013.813976
  10. G. Sudheer and A. Suseelatha, "Short term load forecasting using wavelet transform combined with Holt-Winters and weighted nearest neighbor models," International Journal of Electrical Power & Energy Systems, Vol.64, pp.340-346, 2015. https://doi.org/10.1016/j.ijepes.2014.07.043
  11. P. Bunnoon, K. Chalermyanont, and C. Limsakul, "The Comparision of Mid Term Load Forecastingbetween Multi-Regional and Whole Country AreaUsing Artificial Neural Network," International Journal of Computer and Electrical Engineering, Vol.2, No.2, pp.334-338, 2010.
  12. T. N. Lam, K. K. Wan, S. Wong, and J. C. Lam, "Impact of climate change on commercial sector air conditioning energy consumption in subtropical Hong Kong," Applied Energy, Vol.87, No.7, pp.2321-2327, 2010. https://doi.org/10.1016/j.apenergy.2009.11.003
  13. K. Pilli-Sihvola, P. Aatola, M. Ollikainen, and H. Tuomenvirta, "Climate change and electricity consumption-Witnessing increasing or decreasing use and costs?" Energy Policy, Vol.38, No.5, pp.2409-2419, 2010. https://doi.org/10.1016/j.enpol.2009.12.033
  14. N. B. Adam, M. Elahee, and M. Dauhoo, "Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process," Energy, Vol.36, No.12 pp.6763-6769, 2011 . https://doi.org/10.1016/j.energy.2011.10.027
  15. P.-C. Chang, C.-Y. Fan, and J.-J. Lin, "Monthly electricity demand forecasting based on a weighted evolving fuzzy neural network approach," International Journal of Electrical Power & Energy Systems, Vol.33, No.1, pp.17-27, 2011. https://doi.org/10.1016/j.ijepes.2010.08.008
  16. P. Bunnoon, "The Multi-Point Values of Appropriate Smoothing Parameters Opt $\lambda$. of HP-filter for Mid-Term Load Forecasting based on Neural Network," International Journal of Engineering and Technology, Vol.5, No.4 pp.3533-3543, 2013.
  17. P. Bunnoon, K. Chalermyanont, and C. Limsakul, "Multi-substation control central load area forecasting by using HP-filter and double neural networks (HP-DNNs)," International Journal of Electrical Power & Energy Systems, Vol.44, No.1, pp.561-570, 2013. https://doi.org/10.1016/j.ijepes.2012.08.002
  18. Z. Chen, S. Yang, and X. Wang, "PLS-SVR optimized by PSO algorithm for electricity consumption forecasting," Applied Mathematics & Information Sciences, Vol.7, No.1L, pp.331-338, 2013. https://doi.org/10.12785/amis/071L43
  19. A. Bagnasco, F. Fresi, M. Saviozzi, F. Silvestro, and A. Vinci, "Electrical consumption forecasting in hospital facilities: An application case," Energy and Buildings, Vol.103, pp.261-270, 2015. https://doi.org/10.1016/j.enbuild.2015.05.056
  20. K. Grolinger, A. L'Heureux, M. A. Capretz, and L. Seewald, "Energy forecasting for event venues: Big data and prediction accuracy," Energy and Buildings, Vol.112, pp.222-233, 2016. https://doi.org/10.1016/j.enbuild.2015.12.010
  21. S. Jurado, À. Nebot, F. Mugica, and N. Avellana, "Hybrid methodologies for electricity load forecasting: Entropy-based feature selection with machine learning and soft computing techniques," Energy, Vol.86, pp.276-291, 2015. https://doi.org/10.1016/j.energy.2015.04.039
  22. J. Moon, J. Park, E. Hwang, and S. Jun, "Forecasting power consumption for higher educational institutions based on machine learning," Journal of Supercomputing, pp.1-23, 2017.
  23. C. Sandels, J. Widén, L. Nordström, and E. Andersson, "Day-ahead predictions of electricity consumption in a Swedish office building from weather, occupancy, and temporal data," Energy and Buildings, Vol.108, pp.279-290, 2015. https://doi.org/10.1016/j.enbuild.2015.08.052
  24. J. Pascual, J. Barricarte, P. Sanchis, and L. Marroyo, "Energy management strategy for a renewable-based residential microgrid with generation and demand forecasting," Applied Energy, Vol.158, pp.12-25, 2015. https://doi.org/10.1016/j.apenergy.2015.08.040
  25. K. M. Powell, A. Sriprasad, W. Cole, and T. F. Edgar, "Heating, cooling, and electrical load forecasting for a large-scale district energy system," Energy, Vol.74, pp.877-885, 2014. https://doi.org/10.1016/j.energy.2014.07.064
  26. E. Wessam, and P. Tzscheutschler, "Short-term smart learning electrical load prediction algorithm for home energy management systems," Applied Energy, Vol.147, pp.10-19, 2015. https://doi.org/10.1016/j.apenergy.2015.01.122
  27. Z. Sun, L. Li, A. Bego, and F. Dababneh, "Customer-side electricity load management for sustainable manufacturing systems utilizing combined heat and power generation system," International Journal of Production Economics, Vol.165, pp.112-119, 2015. https://doi.org/10.1016/j.ijpe.2015.04.002
  28. J. Xie and T. Hong, "Wind Speed for Load Forecasting Models," Sustainability, Vol.9, No.5, pp.795-806, 2017. https://doi.org/10.3390/su9050795
  29. 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, 2016. https://doi.org/10.3745/KTCCS.2016.5.10.293