The Design of Direct Load Control System Using Weather Sensors

기상센서를 이용한 지능형 직접부하제어 시스템 디자인 설계

  • 최상열 (인덕대학교 메카트로닉스과)
  • Received : 2015.11.20
  • Accepted : 2015.12.24
  • Published : 2015.12.31

Abstract

The electric utility has the responsibility of reducing the impact of peaks on electricity demand and related costs. Therefore, they have introduced Direct Load Control System (DLCS) to automate the external control of shedding customer load that it controls. The existing DLCS have been operated only depend on On/Off signal from the electric utility. That kind of DLCS operating has been successfully used until now. But since the number of customer load participating in the DLC program are keep increasing, On/Off signal control from the electric utility is no longer meets the needs of many different kind of customers. Therefore, In this paper, the author suggest the design of direct load control system using weather sensors to meet the diversity of different customer needs.

건물 외부에 설치된 각종 기상 측정센서에서 전송된 현재의 외부 기상조건과 일자별, 특수일별 건물 에너지 사용량과의 관계를 인공지능기법으로 분석하고 학습을 통한 예측기능을 갖도록 함으로써, 일자별, 특수일별, 계절별 그리고 기상조건에 따른 익일 전력 사용량을 예측하고 이에 따른 부하의 On/Off 우선순위를 결정하는 기능을 갖는 지능형 직접부하제어 시스템 구조를 설계한다.

Keywords

References

  1. Y. N. Im, "study on information design for developing main screen interfaces of building energy management system", Graduate School of Hongik University, 2013.
  2. S. Y. Kang, "Optimized Facility Control for Energy Saving in Smart Building", Journal of Korean Institute of Information Technology. Vol. 9, No. 2, pp.25-30, 2011
  3. I. S. Hong, "Intelligent building energy management system for efficient energy management", Graduate School of Chung-Ang University, 2013.
  4. C. Kim, "A Study on the Smart Device Monitoring System and the Implementation of Integrated Sensor for BEMS based on Green IT" Graduate School of Hongik University, Seoul National University of Science and Technology, 2013.
  5. E.A. Feinberg and D. Genethliou "Load forecasting In: AppliedMathematics for Restructured Electric Power Systems": Optimization, Control, and Computational Intelligence, Springer, 2005
  6. Jiawei Han, Micheline Kamber, "Data mining : concepts and techniques", Free Academics, Sep. 2003.
  7. S. Y. Choi, H. J. Kim, "Short-term demand forecasting Using Data Mining Method", Journal of the korean Institute of illuninating and Electrical Engineers, Vol. 21, No. 10, pp.126-133, Dec. 2007.