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Multi-zone Building Control System for Energy and Comfort Management

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Hybrid Intelligent Systems (HIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 420))

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Abstract

Smart buildings are a trend of next-generation’s buildings, which allow people to enjoy more convenience, comfort and energy savings. One of the most important challenges on smart and energy-efficient buildings is to minimize the building energy consumption without compromising human comfort. This study proposes a multi-zone building control system coupled with an intelligent optimizer for effective energy and comfort management. Particle swarm optimization (PSO) is utilized to optimize the overall system and enhance the intelligent control of the building in multiple zones. Experimental results and comparisons with other approaches demonstrate the overall performance and potential benefits of the proposed system.

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References

  1. Dounis, A.I., Caraiscos, C.: Advanced control systems engineering for energy and comfort management in a building environment—a review. Renew. Sustain. Energy Rev. 13(6–7), 1246–1261 (2009)

    Google Scholar 

  2. Fong, K., Hanby, V., Chow, T.: System optimization for HVAC energy management using the robust evolutionary algorithm. Appl. Therm. Eng. 29, 2327–2334 (2009)

    Article  Google Scholar 

  3. Kolokotsa, D., Kalaitzakis, G.S., Stavrakakis, K., Agoris, D.: Genetic algorithms optimized fuzzy controller for the indoor environmental management in buildings implemented using PLC and local operating networks. Eng. Appl. Artif. Intell. 15, 417–28 (2002)

    Google Scholar 

  4. Pervez, P.H., Nor, N.M., Nallagownden, P., Elamvazuthi, I.: Intelligent optimized control system for energy and comfort management in efficient and sustainable buildings. Procedia Technol. 11, 99–106 (2013)

    Google Scholar 

  5. Ali, S., Kim, D.: Energy conservation and comfort management in building environment. Int. J. Innov. Comput. Inf. Control 2229–2244 (2013)

    Google Scholar 

  6. Kennedy, J., Eberhart, R.C.: Swarm Intelligence. Morgan Kaufmann, San Mateo (2001)

    Google Scholar 

  7. Meuth, R., Lim, M.H., Ong, Y.S., Wunsch, D.C.: A proposition on memes and meta-memes in computing for higher-order learning. Memetic Comput. 1(2), 85–100, (2009)

    Google Scholar 

  8. Cao, Q., Lim, M.H., Li, J.H., Ong, Y.S., Ng, W.L.: A context switchable fuzzy inference chip. IEEE Trans. Fuzzy Syst. 14(4), 552–567 (2006)

    Google Scholar 

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Acknowledgement

The authors gratefully acknowledge the funding provided by MOE Innovation Fund, Singapore.

Author information

Authors and Affiliations

  1. School of Engineering, Nanyang Polytechnic, 180, Ang Mo Kio Ave 8, Singapore, 569830, Singapore

    Ee May Kan, Siew Leong Kan & Ngee Hoo Ling

  2. Singapore Green Building Council, Singapore, Singapore

    Yvonne Soh & Matthew Lai

Authors
  1. Ee May Kan
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  2. Siew Leong Kan
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  3. Ngee Hoo Ling
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  4. Yvonne Soh
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  5. Matthew Lai
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Corresponding author

Correspondence to Ee May Kan .

Editor information

Editors and Affiliations

  1. Machine Intelligence Research Labs (MIR Labs), Scientific Network for Innovation and Research Excellence, Auburn, Washington, USA

    Ajith Abraham

  2. Department of Computer Science and Engineering, Chung-Ang University, Dongjak-gu, Korea (Republic of)

    Sang Yong Han

  3. Department of Computer Science, Gulf University for Science and Technology, Kuwait, Kuwait

    Salah A. Al-Sharhan

  4. Department of Computer, Shcol of Computer Science and Engineering, Dalian Maritime University, Dalian, China

    Hongbo Liu

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© 2016 Springer International Publishing Switzerland

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Kan, E.M., Kan, S.L., Ling, N.H., Soh, Y., Lai, M. (2016). Multi-zone Building Control System for Energy and Comfort Management. In: Abraham, A., Han, S., Al-Sharhan, S., Liu, H. (eds) Hybrid Intelligent Systems. HIS 2016. Advances in Intelligent Systems and Computing, vol 420. Springer, Cham. https://doi.org/10.1007/978-3-319-27221-4_4

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  • DOI: https://doi.org/10.1007/978-3-319-27221-4_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-27220-7

  • Online ISBN: 978-3-319-27221-4

  • eBook Packages: EngineeringEngineering (R0)

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