A neural network evaluation model for individual thermal comfort

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

“…In recent years, an increasing number of studies [17,[19][20][21][22][23][24][25] have attempted to develop different forms of personal comfort models in order to describe unique comfort characteristics of individual occupants based on the data collected from the actual spaces. These models predict individuals' thermal comfort by correlating environmental measurements with occupant feedback obtained via survey.…”

“…These models predict individuals' thermal comfort by correlating environmental measurements with occupant feedback obtained via survey. They employed various machine learning algorithms such as support vector machine [21,24], neural networks [19], fuzzy rules [26], logistic regression [20], Gaussian process [25], and Bayesian network [17,23] for their model development to improve data representations and predictive performance. The results showed significantly improved predictive accuracy (17-40% gain) compared to conventional comfort models (PMV, adaptive), reinforcing the need for an individualized approach to predict thermal comfort.…”

Artificial Neural Network Estimation of Thermal Insulation Value of Children’s School Wear in Kuwait Classroom

“…Few neural network studies concerned predicting the occupant's thermal comfort conditions inside buildings [13,14]. The occupant's clothing thermal insulation is estimated in these studies; nevertheless, to the best knowledge of the authors no neural networks models were designed to predict the occupant's clothing thermal insulation.…”

Approximate Dynamic Programming for Building Control Problems with Occupant Interactions

“…In particular, the thermal preferences of occupants induce their actions, which potentially perturb the thermal dynamics of building spaces. It is a special class of stochastic systems in the sense that the statistical behavior of the occupant's actions interact with the system evolution: occupant thermal preference models [6][7][8] depend on environmental factors, for example, the indoor air temperature. For this reason, developments of effective stochastic control methods become of prime importance.…”

“…Challenges: In building environment research, advanced occupant thermal preference models have been developed, e.g., [6][7][8], where occupant's thermal preferences are expressed as probability mass functions that depend on environmental factors, for example, the indoor air temperature. However, the existing results did not consider such occupant behavior models that interacts with the building dynamics.…”