Browse > Article
http://dx.doi.org/10.5573/ieie.2016.53.10.051

Implementation and Performance Analysis of An Optimal Energy Management System Using Data Inference and Cloud Hosting Scheme  

Kim, Kyung-Shin (ChungKang College of Cultural Industries)
Kang, Moon-Sik (GangneungWonju National University)
Publication Information
Journal of the Institute of Electronics and Information Engineers / v.53, no.10, 2016 , pp. 51-57 More about this Journal
Abstract
In this paper, we propose an optimal energy management system using the data inference scheme and the cloud hosting technique in order to improve the efficiency of the energy management. We have been interested in the issue that the energy-saving and efficient management techniques are very useful for reducing the production and supply of energy. The energy management system refers to the control and management system in order to enable the efficient use of energy and also to maintain a comfortable and functional working environment effectively with the help of a computer. The proposed system controls a variety of equipment for energy management, and also gets the data for the inference from the changes in energy consumption environment, which is implemented to enable efficient energy management by adapting and controlling the changes optimally in the working environment. In order to evaluate the performance of the implemented system, some experiments have been performed under consideration of the monthly electric power consumption on the server that the inference engine is operating for the target facilities. Finally, the results show that the proposed system has a good performance.
Keywords
데이터 추론기법;에너지 관리시스템;클라우드 호스팅;전력사용량;최적 제어;
Citations & Related Records
연도 인용수 순위
  • Reference
1 M. Wetter, "Building control virtual test bed user manual version 1.1.0", Lawrence Berkeley National Laboratory, 2012.
2 Ye Yao, Zhiwei Lian, Zhijian Hou, X. Zhou, "Optimal operation of a large cooling system based on an empirical model", Applied Thermal Engineering 24, pp. 2303-2321, 2004.   DOI
3 K. S. Kim et al, "Implementation of Location Tracking Sensor Network Using M2M Technology & Cloud Services", Journal of IEIE, pp. 93-102, Sep. 2014.
4 Rafael Alcala, Jorge Casillas, Oscar Cordon, Antonio Gonzalez, Francisco Herrera, "A genetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning system", Engineering Appllicaion Artificial Intelligence 18, pp. 279-296, 2005.   DOI
5 W. K. Park, Y. K. Jeong, I. W. Lee, "Management Technology for High Energy-Efficient Building", ETRI Electronics and Telecomm. Trend 26(6), 2011.
6 K. S. Kim, M.S. Kang, "A Study on An Efficient Electricity Saving Scheme for Mobile Cloud Computing", Proceedings of ICGHIT 2016 (IEIE CS & IEEE Seoul Sec.), Feb. 2016.
7 Anton Beloglazov, "Energy-Efficient Management of Virtual Machines in Data Centers for Cloud Computing", PhD thesis of The Univ. of MELBOURNE, Feb. 2013.
8 K. S. Kim et al,, "Design of Smart Management System Based on Energy Saving & Big Data Analysis", Proceedings of 2015 Fall Conference of IEIE, pp. 712-714, Nov. 2015.
9 Yusuf Ozturk, "An Intelligent Home Energy Management System to Improve Demand Response", IEEE Transactions on Smart Grid, Vol. 4, pp. 694-701. June 2013.   DOI