Genetic algorithm for building optimization - State-of-the-art survey

Tiejun Li, Guifang Shao, Wangda Zuo, Sen Huang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

8 Scopus citations

Abstract

Model-based building operation optimization can be used to reduce building energy consumption, so as to improve the indoor environment quality. Genetic Algorithm (GA) is one of the commonly used optimization algorithms for building applications. To provide readers up-to-date information, this paper attempts to summarize recent researches on building optimization with GA. Firstly, the principle of GA is introduced. Then, we summarize the literatures according to different categories, including applied system types and optimization objectives. We also provide some insights into the parameter setting and operator selection for GA. This review paper intends to give a better understanding and some future directions for building research community on how to apply GA for building energy optimization.

Original languageEnglish (US)
Title of host publicationProceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017
PublisherAssociation for Computing Machinery
Pages205-210
Number of pages6
VolumePart F128357
ISBN (Electronic)9781450348171
DOIs
StatePublished - Feb 24 2017
Event9th International Conference on Machine Learning and Computing, ICMLC 2017 - Singapore, Singapore
Duration: Feb 24 2017Feb 26 2017

Other

Other9th International Conference on Machine Learning and Computing, ICMLC 2017
CountrySingapore
CitySingapore
Period2/24/172/26/17

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Keywords

  • Building
  • Energy
  • Genetic algorithm
  • Optimization

ASJC Scopus subject areas

  • Human-Computer Interaction
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Software

Cite this

Li, T., Shao, G., Zuo, W., & Huang, S. (2017). Genetic algorithm for building optimization - State-of-the-art survey. In Proceedings of 2017 9th International Conference on Machine Learning and Computing, ICMLC 2017 (Vol. Part F128357, pp. 205-210). Association for Computing Machinery. https://doi.org/10.1145/3055635.3056591