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Research on the experiment and prediction method of clothing energy consumption based on TS-SVR

Research on the experiment and prediction method of clothing energy consumption based on TS-SVR At present, the research on energy consumption of human clothing mainly focuses on behavior observation method, questionnaire survey method, heart rate monitoring method and electronic motion sensor, etc. In order to solve the problem of energy consumption caused by clothing with different characteristics, an identification method of energy consumption for different types of clothing was proposed.Design/methodology/approachThe model robot was designed to reproduce the motion state by simulating the human body in the working mode, and the protective energy consumption test platform was built. In order to explore the influence of different characteristics of clothing on the energy consumption of equipment system, orthogonal experiments were carried out on the model robot experimental platform, and a mathematical model for predicting the energy consumption of clothing based on Tabu search algorithm to optimize support vector machine regression (TS-SVR) optimized by tabu algorithm was proposed.FindingsCompared with three regression prediction algorithms, the accuracy of the model was quantified by the determination coefficient and root mean square error according to the predicted value of the model and the actual value of the experiment. The results showed that the model based on TS-SVM can predict the energy consumption of human body more accurately.Originality/valueBased on TS-SVR model, it can well predict the relationship between clothing with different characteristics and physical energy consumption, and can accurately evaluate the clothing grade of different characteristics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Clothing Science and Technology Emerald Publishing

Research on the experiment and prediction method of clothing energy consumption based on TS-SVR

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References (19)

Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
0955-6222
DOI
10.1108/ijcst-05-2021-0069
Publisher site
See Article on Publisher Site

Abstract

At present, the research on energy consumption of human clothing mainly focuses on behavior observation method, questionnaire survey method, heart rate monitoring method and electronic motion sensor, etc. In order to solve the problem of energy consumption caused by clothing with different characteristics, an identification method of energy consumption for different types of clothing was proposed.Design/methodology/approachThe model robot was designed to reproduce the motion state by simulating the human body in the working mode, and the protective energy consumption test platform was built. In order to explore the influence of different characteristics of clothing on the energy consumption of equipment system, orthogonal experiments were carried out on the model robot experimental platform, and a mathematical model for predicting the energy consumption of clothing based on Tabu search algorithm to optimize support vector machine regression (TS-SVR) optimized by tabu algorithm was proposed.FindingsCompared with three regression prediction algorithms, the accuracy of the model was quantified by the determination coefficient and root mean square error according to the predicted value of the model and the actual value of the experiment. The results showed that the model based on TS-SVM can predict the energy consumption of human body more accurately.Originality/valueBased on TS-SVR model, it can well predict the relationship between clothing with different characteristics and physical energy consumption, and can accurately evaluate the clothing grade of different characteristics.

Journal

International Journal of Clothing Science and Technology – Emerald Publishing

Published: Sep 26, 2022

Keywords: Clothing energy consumption; Support vector machine regression; Test platform; Tabu search; Grade evaluation; Prediction

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