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

Korea Information Processing Society (한국정보처리학회)
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An Electric Load Forecasting Scheme with High Time Resolution Based on Artificial Neural Network

인공 신경망 기반의 고시간 해상도를 갖는 전력수요 예측기법

  • 박진웅 (고려대학교 전기전자공학과) ;
  • 문지훈 (고려대학교 전기전자공학과) ;
  • 황인준 (고려대학교 전기전자공학과)
  • Received : 2017.07.12
  • Accepted : 2017.09.04
  • Published : 2017.11.30
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Abstract

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.

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.

Keywords

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