Earticle

초록

영어

This paper presents the development of Short Term Load Forecasting (STLF) model using Artificial Neural Network (ANN). STLF is required for electric power planning and electricity market planning. The proposed model predicts the load demand of Connecticut in the U.S. using hourly historical electric load and weather data. For improving the load prediction accuracy, we consider two main issues that are seasons and weather factors. Each season has different load demand patterns, thus the weather factors are differently applied in each season. The proposed model uses the composited weather factor which consists of temperature and dew point. The temperature and dew point weather factors are selected through the correlation coefficient to obtain the meaningful data among the weather factors. The selected weather factors adjust the level of the pitch which is the predicted load demand of one day ahead. The proposed model improves the forecasting accuracy both in summer and winter.

목차

Abstract
 1. Introduction
 2. Backpropagation Learning Algorithm
 3. Input Variables Selection
 4. Temperature Weight Generation
 5. Experiments and Results
 6. Conclusions
 Acknowledgements
 References

키워드

저자

• D. Kown [ Department of Computer Science, Sangmyung University, Seoul, Korea ]
• M. Kim [ Department of Computer Science, Sangmyung University, Seoul, Korea ]
• C. Hong [ Department of Computer Science, Sangmyung University, Seoul, Korea ] Corresponding Author
• S. Cho [ Department of Energy Grid, Sangmyung University, Seoul, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605