Journal of the Institute of Electronics and Information Engineers (전자공학회논문지)

The Institute of Electronics and Information Engineers (대한전자공학회)
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The Artificial Neural Network based Electric Power Demand Forecast using a Season and Weather Informations

계절 및 날씨 정보를 이용한 인공신경망 기반 전력수요 예측 알고리즘 개발

  • Kim, Meekyeong (Department of Computer Science, Sangmyung University) ;
  • Hong, Chuleui (Department of Computer Science, Sangmyung University)
  • 김미경 (상명대학교 컴퓨터과학과) ;
  • 홍철의 (상명대학교 컴퓨터과학과)
  • Received : 2015.05.07
  • Accepted : 2015.12.23
  • Published : 2016.01.25
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Abstract

This paper proposes the new electric power demand forecast model which is based on an artificial neural network and considers time and weather factors. Time factors are selected by measuring the autocorrelation coefficients of load demand in summer and winter seasons. Weather factors are selected by using Pearson correlation coefficient The important weather factors are temperature and dew point because the correlation coefficients between these factors and load demand are much higher than those of the other factors such as humidities, air pressures and wind speeds. The experimental results show that the proposed model using time and seasonal weather factors improves the load demand forecasts to a great extent.

본 논문은 인공 신경망에 기반을 둔 새로운 전력 수요 예측 모델을 제시한다. 인공 신경망 입력 변수로 시간과 날씨요소를 고려하였다. 시간 요소는 하절기와 동절기 전력수요 데이터의 자기 상관계수를 측정하여 선정하였고, 날씨요소는 피어슨 상관계수를 이용하여 선정하였다. 중요한 날씨요소로는 온도와 이슬점으로 이들은 전력수요와 밀접한 상관관계를 가지고 있다. 반면에 습도, 기압, 풍속 등과 같은 날씨요소는 전력수요와의 상관관계가 높지 않게 나타나 신경망의 입력 변수에서 제외하였다. 실험결과 새로이 제안한 인공 신경망을 이용한 전력수요 모델은 시간요소 및 날씨요소와 이에 대한 가중치를 피크 전력율과 계절에 따라 차등 적용하여 높은 적중률을 보였다.

Keywords

References

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