Investigation of occupants' characteristics impact on thermal comfort assessment using a novel neural network PMVo calculation model.

The main aim of this study is the analysis of the impact that occupants' characteristics have on thermal comfort assessment, through establishing a novel PMVo model using an approximation method, based on the experimental data. The parameters which are chosen as model's inputs are the air temperature, mean radiant temperature, relative humidity, basic clothing insulation, air velocity and occupants characteristics – gender, age, height, and body mass, while the output is the PMVo, a novel thermal comfort index. Since existing standards concerning thermal comfort do not consider these occupants' characteristics, the main novelty of the introduced model is the inclusion of occupants' characteristics in the thermal comfort assessment. To ensure enhanced precision, the model is established using both linear regression and by training neural network. These two approximation methods are compared to determine which one is more applicable in the context of data approximation. Study shows that regardless of dataset based on which models are established and regardless of testing input values, neural network (R2 in the range of 99.87% to 99.96%) is a superior mathematical approximation algorithm compared to the linear regression (R2 in the range of 95.3% to 97.5%). Novel neural network based thermal comfort assessment model is used for investigation of occupants' characteristics impact on thermal comfort assessment. Analysis of the results showed that gender, age, height and body mass may significantly impact thermal comfort indices calculation, which implies the necessity of their inclusion in thermal comfort prediction and evaluation. Thus, the presented PMVo model may be highly beneficial to implement within existing thermal comfort standards, ensuring well-being and satisfaction with conditions of indoor environment for wider range of the occupants. [ABSTRACT FROM AUTHOR]