Evaluation of thermal comfort in air-conditioned rooms based on structure/control-related parameters and data-mining method

Abstract

Evaluating the thermal environment and thermal comfort in an air-conditioned room is an essential for estimating the performance of air-conditioning systems. However, multiple component structures and control-related parameters often lead to a long test cycle and large number of tests, significantly affecting the testing efficiency and speed. To address these problems, in this study, a data-mining method was proposed to predict and evaluate the thermal environment of an air-conditioned room. Owing to the limited amount of experimental data, the sample data were expanded by the simulation data of a collaborative platform between the air-conditioning system and air-conditioned room. Data-mining models, including the support vector regression (SVR), backpropagation (BP), and multiple linear regression (MLR) models, were developed and achieved good accuracy in evaluating the thermal environment by considering air-conditioning systems with various structures and control parameters. In the multiple-input single-output evaluation method, the prediction accuracy of the SVR model was higher than those of the BP and MLR models with respect to the vertical air temperature difference, temperature uniformity, temperature drop rate, and draft rate, while the result was the opposite in terms of the predicted mean vote indices. In the multiple-input multiple-output evaluation method, there was a decline in prediction accuracy and an increase in efficiency prediction compared with multiple-input single-output evaluation.