Abstract
Machine learning technology has become a hot topic and is being applied in many fields. However, in the prediction of thermal sensation in the elderly, there is not enough research on the neural network to predict the effect of human thermal comfort. In this paper, two neural network algorithms were used to predict the thermal expectation of the elderly, and the accuracy of the two algorithms was compared to find a suitable neural network algorithm to predict human thermal comfort. The dataset was collected from the laboratory study and included 10 local skin temperatures of the subjects, thermal perception voted at three temperatures (28/30/32°C), different wind speeds, and two forms of wind. Thirteen subjects with an average age of 63.5 years old were recruited for the subjective survey. These subjects sat for long periods of summer working conditions, wore uniform thermal resistance clothing, and collected votes on thermal sensation, as well as skin temperature. The results showed that the prediction accuracy of the two algorithms was related to the added influence factors, and the RBF neural network algorithm was the most accurate in predicting thermal sensation of the elderly. The main influencing factors were average skin temperature, wind speed and body fat rate.
Highlights
With the development of computer and artificial intelligence, data acquisition is becoming more and more easy due to the large number of data monitoring systems and acquisition platforms, and the application of machine learning algorithms in the field of thermal comfort is attracting more and more attention
Subjects were recruited for a subjective survey in which they sat for a long time, wearing a uniform thermal resistance of 0.5 clo to collect thermal sensation votes, and skin temperatures in ten body parts
By sorting the experimental data, the average skin temperature of the subjects and the corresponding thermal sensation were plotted as Figure 3.Figure in white dots represent the median, the subjects in thermal sensation vote in feel cold, neutral, hot, median average skin temperature, respectively is 32.89, 33.25, 33.78°C, the skin temperature can be obtained, the higher the participants feel more warm, and each group of data is normally distributed, can prove the effectiveness of the experimental data
Summary
With the development of computer and artificial intelligence, data acquisition is becoming more and more easy due to the large number of data monitoring systems and acquisition platforms, and the application of machine learning algorithms in the field of thermal comfort is attracting more and more attention. Some scholars found that older people had higher perceptual thresholds and lower sensitivity to cold and warm environment than younger people[1]. Some people found that in warm and cold environments, older people typically had a thermal sensation 0.5 units lower than younger people[2]. This suggested that older people respond differently to thermal stimuli. In order to fill this gap, the goal of this paper is to use neural network to build a thermal sensation model for the elderly to predict their thermal responses under different thermal conditions
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