Investigating spatial impact on indoor personal thermal comfort

Thermal comfort prediction is essential for both maintaining a favorable indoor environment and reducing energy consumption. Predicted Mean Vote (PMV), as the most popular research method, has a limitation in processing various complex parameters and investigating the individual difference in occupants' thermal preference. Therefore, machine learning (ML) method has been utilized in exploring the personal thermal comfort prediction because of its strong self-study ability, high-speed computing ability, and complex problem-solving ability. However, the primary variables considered in previous studies focus on the human body's physiological and psychological aspects, while lack of considering architectural spatial impact, which causes different indoor microclimate. Therefore, the present research proposed a methodology to investigate the impact from spatial parameters on personal thermal comfort prediction model accuracy by developing an ANN-based model and explicitly representing the spatial variables in the model. The spatial parameters were identified and classified into buildings' spatial features, indoor spatial features and individual spatial features. The data required in developing the ANN-based model were collected by various field experiments. A baseline of model prediction accuracy was calculated by using conventional parameters, including personal-dependent parameters and environmental parameters. It was found that the spatial parameters had a noticeable impact on model prediction accuracies. By considering spatial parameters in the ANN-based model development, the prediction accuracies had been increased significantly compared with the conventional models.

Automated summary

Thermal comfort prediction is crucial for maintaining indoor comfort and reducing energy consumption. Machine learning methods have been used to explore personal thermal comfort prediction, and it was found that spatial parameters significantly impact model accuracy. By considering spatial parameters, prediction accuracies have been significantly improved compared to conventional models.