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

Purpose 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/approach The 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. Findings Compared 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/value Based 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.