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Soft Computing
Published in: Soft Computing 11/2013

01-11-2013 | Focus

Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method

Authors: Chengdong Li, Guiqing Zhang, Ming Wang, Jianqiang Yi

Published in: Soft Computing | Issue 11/2013

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Abstract

In the research domain of intelligent buildings and smart home, modeling and optimization of the thermal comfort and energy consumption are important issues. This paper presents a type-2 fuzzy method based data-driven strategy for the modeling and optimization of thermal comfort words and energy consumption. First, we propose a methodology to convert the interval survey data on thermal comfort words to the interval type-2 fuzzy sets (IT2 FSs) which can reflect the inter-personal and intra-personal uncertainties contained in the intervals. This data-driven strategy includes three steps: survey data collection and pre-processing, ambiguity-preserved conversion of the survey intervals to their representative type-1 fuzzy sets (T1 FSs), IT2 FS modeling. Then, using the IT2 FS models of thermal comfort words as antecedent parts, an evolving type-2 fuzzy model is constructed to reflect the online observed energy consumption data. Finally, a multiobjective optimization model is presented to recommend a reasonable temperature range that can give comfortable feeling while reducing energy consumption. The proposed method can be used to realize comfortable but energy-saving environment in smart home or intelligent buildings.
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Metadata
Title
Data-driven modeling and optimization of thermal comfort and energy consumption using type-2 fuzzy method
Authors
Chengdong Li
Guiqing Zhang
Ming Wang
Jianqiang Yi
Publication date
01-11-2013
Publisher
Springer Berlin Heidelberg
Published in
Soft Computing / Issue 11/2013
Print ISSN: 1432-7643
Electronic ISSN: 1433-7479
DOI
https://doi.org/10.1007/s00500-013-1117-4

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