We developed a model for predicting thermal sensation using a neural network (NN) considering secondary comfort factors. This study revealed the following three main points. 1. The prediction accuracy of the NN model is higher than that of the PMV, and the accuracy is also higher for naturally ventilated conditions. 2. Although ventilation condition improves the prediction accuracy, this effect disappears when considering outdoor and climatic factors. 3. While personal factors of age and gender and seasonal factors of date and season improve prediction accuracy, they have little power on prediction when considering climate and other factors.
|Translated title of the contribution||THERMAL SENSATION PREDICTION USING NEURAL NETWORK CONSIDERING SECONDARY COMFORT FACTORS|
|Number of pages||8|
|Journal||Journal of Environmental Engineering (Japan)|
|Publication status||Published - 2022|
All Science Journal Classification (ASJC) codes
- Environmental Engineering