Evaluation of Indoor Thermal Environments Using a Novel Predicted Mean Vote Model Based on Artificial Neural Networks

The assessment of indoor thermal environments is crucial to achieving thermal comfort and energy efficiency. However, the inaccurate evaluation and strong nonlinear variations of thermal comfort parameters limit engineering designs. Therefore, a coupled heat-transfer model was developed in this study, and large eddy simulations were performed to verify the influence of inertia and buoyancy—which are mutually exclusive forces but coexist in large-space building environments—on inhomogeneous thermal environments. Furthermore, an artificial neural network (ANN) model was designed to overcome the limitations of the nonlinear relationships between thermal parameters and predicted mean vote (PMV) values. PMV indexes can be predicted using the ANN model when thermal parameters are used as input data. Subsequently, a genetic algorithm, harmony search algorithm, gravitational search algorithm, and whale optimization algorithm were adopted to optimize the neural network structure to prevent its confinement in a local optimum. Finally, with 5000 data sets, the minimum-error neural network structure 6-22-23-1 of the ANN-GA neural network model had high prediction accuracy, mean relative error < 1.38, root mean square error < 1.34, and a regression coefficient of ~1. The proposed ANN model can help improve the assessment of the thermal environment and thermal comfort of buildings.

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