Neural network-based thermal comfort prediction for the elderly

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.

A Review of Thermal Comfort in Primary Schools and Future Challenges in Machine Learning Based Prediction for Children

“…Neural Networks can be deep or shallow depending on the number of layers in the ML model. Shallow NN algorithms, such as Artificial Neural Networks [87,93,97,105,106,[114][115][116] and Bayesian Neural Networks [117], are increasingly being applied to TC modeling and prediction. Deep learning represents a problem through a nested hierarchy or hidden layers of interconnected neural networks through which abstract categories can be computed from less abstract ones [15].…”

Individual thermal comfort models based on optimized BP neural network algorithms

“…Several studies have therefore combined ANN with predicting thermal comfort to save energy. Zhang et al employed two neural network algorithms, which not only predicted the thermal expectation of the elderly but also compared the accuracy of those models [8]. Similarly, * Corresponding author: yangbin@xauat.edu.cn (B. Yang) Xue et al proposed a BP model to predict the energy performance, predicted mean vote and indoor air quality, and draft rate [9].…”