Analysis of the impact of indoor thermal comfort data characteristics on dataset quality

Abstract

The impact of data characteristics on dataset quality is of significant guidance in the field of thermal comfort field surveys. In this paper, a Chinese thermal comfort database has been established, comprising quality-controlled on-site survey data. The database encompasses 41,977 sets of on-site survey data collected from 50 cities across 24 provinces, spanning a period of nearly 20 years from 2001 to 2021. It includes the five thermal zones in China and encompasses three seasons—summer, transitional, and winter—as well as four types of buildings: office buildings, residential buildings, student dormitories, and classrooms. The database exhibits an extensive data distribution, demonstrating a certain level of representativeness in terms of environmental temperature and survey locations. Based on this established database, the thermal sensation model was employed as the criterion for evaluating dataset quality. This study analyzed the impact of data characteristics on dataset qualityfrom three perspectives: sample size, data distribution, and data range. A sample size calculation method using interval estimation was adopted, determiningthat a minimum sample size of 350 is required for on-site thermal comfort surveys in office and residential buildings. A comparison revealed that compared to data with the normal distribution and uniform distribution, data with the positively skewed distribution exhibit lower neutral temperatures, while data with the negatively skewed distribution exhibit higher neutral temperatures. The thermal sensation model constructed based on data with uniform distribution demonstrates greater robustness. As the temperature range decreases, the neutral temperature increases in winter and decreases in summer, resulting in reduced accuracy of the constructed thermal sensation model.