Earticle

초록

영어

In this paper, the problem of short-term load forecasting is divided into load classification and forecasting. Load classification is needed to obtain meaningful load data as input to train forecasting models. To this end, k-NN and K-mean algorithms are presented. K-mean and k-NN algorithms can handle seasonal load classification and daily load classification, respectively. The classified load data are used to train forecasting models, which are Artificial Neural Networks, Simple Exponential Smoothing, and ARIMA models. As a real case study, we tried to forecast the electric power load of the Republic of Korea. A comparison between the classified and non-classified load forecasts demonstrates the efficiency of the proposed method.

목차

Abstract
 1. Introduction
 2. Load Classification
  2.1. K-mean Clustering
  2.2. k-Nearest Neighbor Classification
 3. Load Forecasting
  3.1. Simple Exponential Smoothing
  3.2. Auto Regression Integrated Moving Average Model(ARIMA Model)
  3.3. Artificial Neural Network(ANN)
 4. Case Study
  4.1. Load Classification
  4.2. Load Forecasting
 5. Conclusion
 Acknowledgements
 References

키워드

저자

• Bongil Koo [ Department of electrical and computer engineering, Pusan National Univ. ]
• Juneho Park [ Department of electrical and computer engineering, Pusan National Univ. ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Control and Automation

• 간기

  월간

• pISSN

  2005-4297

• 수록기간

  2008~2016

• 십진분류

  KDC 505 DDC 605