Earticle

초록

영어

In the smart grid, it will be possible to communicate with the consumers for the purposes of monitoring and controlling their power consumption without disturbing their business or comfort. This will bring easier administration capabilities for the utilities. On the other hand, consumers will require more advanced home automation tools which can be implemented by using advanced sensor technologies. For instance, consumers may need to adapt their consumption according to the dynamically varying electricity prices which necessitates home automation tools. This paper tries to combine neural network and nonlinear autoregressive with exogenous variable (NARX) class for next week electric load forecasting. The suitability of the proposed approach is illustrated through an application to electric load consumption data. The suggested system provides a useful and suitable tool especially for the load forecasting.

목차

Abstract
 1. Introduction
 2. NARX model class
 3. Multilayer perceptron RNN identification scheme
 4. Results
 5. Conclusion
 References

키워드

저자

• Sang-Hyun Lee [ Dept. of Computer Engineering, Honam University, Korea ]
• Park Dae-Won [ Darwin Company, Korea ] Corresponding Author
• Kyung-Il Moon [ Dept. of Computer Engineering, Honam University, Korea ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700