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Development of Intelligent Electricity Saving System Using SARIMA Algorithm
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 Title & Authors
Development of Intelligent Electricity Saving System Using SARIMA Algorithm
Heo, Jun; Kim, Kyung-Shin;
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Many people all over the world have been conducting a great deal of research to solve the problem of global warming since the great majority consider reduction of CO2 as the only solution for that. That is why the production and conservation of energy is thought to be highly crucial. while it is important to produce energy with the high efficiency, the efficient use of the energy is also important. This paper focused on the development of devices for the reducing electricity which is a primary energy source used in homes, shops, buildings, factories and so on. Also the objective of this paper is to develop the inference mechanism as the core component of the devices. Therefore, in this paper, we propose the inference algorithm for reducing the electricity consumption using SARIMA mode and present the feasibility of the procedure.
Inference algorithm;SARIMA mode;Reducing electricity consumption;Global warming;
 Cited by
W.K. Park, Y.K. Jeong, I.W. Lee, "Energy Management Technology for High Energy-Efficient Building," ETRI, Electronics and Telecommunications Trends 26(6), 2011.12.

Y.K. Jeong, W.K. Park, J.S. Han, C.S. Choi, H.J. Yoon, I.W. Lee, "An architecture of the remote building management and control platform for high-efficient low-cost building energy management," ETRI, 2010.11.

A.Y. Kim, H.J. Yoon, Y.K. Jeong, I.W. Lee, "A Study on the Problems in Adopting Building Management Systems to Korea and the Suggestions for Improvement," The Korean Institute of Communications and Information Sciences Summer Conference, 2011.

H.J. Lee, J.S. Han, Y.K. Jeong, I.W. Lee, S.H. Lee, "A Technology of Context-aware based Building Management for Energy Efficiency," Journal of IT Convergence Society for SMB Vol.2 No.1, 2012, pp. 69-75.

D.S. Kong, Y.H. Kwak, J.H. Huh, "Artificial Neural Network based Energy Demand Prediction for the Urban District Energy Planning," Journal of Architectural Institute of Korea Vol.29 No.1, January 2013.

G.J. Levermore, " Bulilding Energy Management Systems," E&FN Spoon, 2000.

Honeywell, "Engineering Manuall of Automatic Control", Honeywell, 1988.

Roger W.Haines & Douglas C.Hittle,"Control Systems for Heading," Ventilating and Air Conditioning, Springer, 2006.

Zhun Yu., Fariborz Haghighat, Benjamin C, M. Fung, Hirushi Yoshino,"A decision tree method for bulding energy demand modeling," Energy and Building, 2010.

Xiaoli Li, Chris P. Bowers, Thorsten Schnier,"Classification of Energy Consumption in Building with Outlier Detection," IEEE 2009.

Bing Dong, Cheng Cao, Siew Eang Lee,"Applying support vector machines to predict building energy consumption in tropical region," Energy and Building, 2005.

S.T. Kim "Management System of USN-based Collaborative Lighting Energy," The Journal of The Institute of Webcasting, Internet and Telecommunication Vol.13, No.4, pp.47-53, Dec. 2013.

Y.G. Kim, G.C. Sin, S.K. Kim "Collaboration Interface for Hadoop-based Bigdata Processing Platform," Proceedings of The Korean Institute of Information Technology (KIIT) 2013 summer conference, pp.511-516, 2013.

H.J. Moon, K.A. An, and M.S. Choi, A simulation-based control approach in a mechanical ventilation system, Proceedings of ASim, Shanghai, China, 2012

H.j. Moon and M.S. Choi, The effect of weather data in an integrated real-time building simulation connected to a monitoring system, Proceedings of ISOEN, Daegu, Korea, 2013.

M. Wetter, Building control virtual test bed user manual version 1.1.0, Lawrence Berkeley National Laboratory, 2012.

X. Pang, M. Wetter, P. Bhattacharya, P. Haves, A framework for simulation-based real-time whole building performance assessment, Building and Environment 54, 100-108, 2012. crossref(new window)

Z, O'Neill, M. Shashanka, X. Pang, P. Bhattacharya, T. Bailey, P. Haves, Real-time model based energy diagnostics in buildings, Proceedings of Building Simulation, Sydney, Australia, 2011.

A. K. Dey., "Providing Architectural Support for Building Context-Aware Applications", PhD thesis, Georgia Institute of Technology, Noember, 2000.

S. Sigg, S. L. Lau, S. Haseloff, "Approaching a definition of context prediction", In Proceedings of the Third Workshop on Context Awareness for Proactive Systems, 2007.

Matthew Horideg, Horrideg, Holger Knublauch, Alan Rector, A Practical Guide To Building OWL Ontologies Using The Proteget-OWL Pluging and CO-oDE Tools, The University of Manchester, 2004.

Wang, W., Dong, J.S., Chin, C.Y., "Semantic Space: an infrastructure for smart spaces", Pervasive Computing, IEEE, Vol. 3, Issue 3, pp.32-39, 2004. crossref(new window)

Minsu Jang, Joo-chan Sohn, "Bossam: AnExtended Rule Engine for OWL Inferencing", Proceeding of RileML(LNCS Vol. 3323), 2004.

R. Alcala, J. Casillas, O. Cordon, A. Gonzalez, F. Herrera,"Agenetic rule weighting and selection process for fuzzy control of heating, ventilating and air conditioning systems", Engineering Applications of Artificial Intelligence Vol. 18, pp.279-296, 2005. crossref(new window)

A. Kanarachos, K. Germanis,"Multivariable control of single zone hydronic headting systems with neural networks", Energy Convers. Mgmt Vol. 39, No. 13, pp.1317-1336, 1998. crossref(new window)

Y. Yao, Z. Lian, Z. Hou, X. Zhou,"Optimal operation of a large cooling system based on an empirical model", Applied Thermal Engineering Vol. 24, pp.2303-2321, 2004. crossref(new window)