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Application of Optimized Genetic Algorithm in Building Energy-Saving Optimization Control

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Lecture Notes in Real-Time Intelligent Systems (RTIS 2016)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 613))

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Abstract

As the concept of low carbon and green enjoys popular support, how to construct an energy-saving building system in the modern building project has gradually become a research hotspot in the industry. Based on the above background, this paper proposed an adaptive loop optimized genetic algorithm. On the basis of fully expounding the principles and advantages of this algorithm, this paper took residential housing in Xiangyang for example, introduced such optimized genetic algorithm and constructed a full set of building energy-saving optimization control system. The calculation and application results indicate that this optimized genetic algorithm can achieve bi-directional optimal control of building energy consumption and construction cost, and has good Adaptivity and portability.

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References

  1. Ballarini, I., et al.: Use of reference buildings to assess the energy saving potentials of the residential building stock: the experience of TABULA project. Energy Policy 68, 273–284 (2014)

    Article  Google Scholar 

  2. Gossard, D., et al.: Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network. Energy Build. 67, 253–260 (2013)

    Article  Google Scholar 

  3. Heo, Y., et al.: Calibration of building energy models for retrofit analysis under uncertainty. Energy Build. 47, 550–560 (2012)

    Article  Google Scholar 

  4. Huang, J., et al.: Thermal properties optimization of envelope in energy-saving renovation of existing public buildings. Energy Build. 75, 504–510 (2014)

    Article  Google Scholar 

  5. Cabeza, L.F., et al.: Life cycle assessment (LCA) and life cycle energy analysis (LCEA) of buildings and the building sector: a review. Renew. Sustain. Energy Rev. 29, 394–416 (2014)

    Article  Google Scholar 

  6. Wang, D., et al.: Data filtering based recursive least squares algorithm for Hammerstein systems using the key-term separation principle. Inf. Sci. 222, 203–212 (2013)

    Article  MathSciNet  MATH  Google Scholar 

  7. Lu, H.-L., et al.: A self-adaptive genetic algorithm to estimate JA model parameters considering minor loops. J. Magn. Magn. Mater. 374, 502–507 (2015)

    Article  Google Scholar 

  8. Gu, Q., Wang, X.-M., Wu, Z., Ning, B., Xin, C.-S.: An improved SMOTE algorithm based on genetic algorithm for imbalanced data classification. J. Dig. Inf. Manag. 14(2), 92–103 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Xiamen Institute of Software Technology, Xiamen, China

    Meie Lin

Authors
  1. Meie Lin
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Correspondence to Meie Lin .

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Editors and Affiliations

  1. Institute of Mathematics and Computer Science, Opole University , Opole, Opolskie, Poland

    Jolanta Mizera-Pietraszko

  2. Digital Information Research Foundation , Chennai, Tamil Nadu, India

    Pit Pichappan

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Lin, M. (2018). Application of Optimized Genetic Algorithm in Building Energy-Saving Optimization Control. In: Mizera-Pietraszko, J., Pichappan, P. (eds) Lecture Notes in Real-Time Intelligent Systems. RTIS 2016. Advances in Intelligent Systems and Computing, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-60744-3_20

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  • DOI: https://doi.org/10.1007/978-3-319-60744-3_20

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-60743-6

  • Online ISBN: 978-3-319-60744-3

  • eBook Packages: EngineeringEngineering (R0)

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