| 摘要: |
| 适应性热舒适（ATC）模型可以通
过规定建筑全年设定点的温度来实现节能的
目的。已有模型自变量的代表值不统一，采
用不同的代表值建立的模型差异很大。如何
确定合适的代表值以建立最优模型需要进一
步研究。本文提出一种优化ATC 模型的方
法，并通过案例实现并验证该方法的有效
性。研究采用仪器测试和主观问卷结合的方
法进行一年的热舒适调查，建立不同代表值
的ATC模型，并将优化后的模型与其他模型
进行对比。研究得到以下结论：提出ATC模
型的优化方法被实现并证实其有效；研究得
到的模型优于其他已有模型；对比全年模
型，分类模型的预测精度和稳定性均有提
高，其中季节模型的提高幅度大，性别模型
的提高幅度小。本研究结果为准确建立ATC
模型提供参考。 |
| 关键词: 适应性热舒适模型 灰色关联 中
性温度 自然通风建筑 统计分析 |
| DOI：10.13791/j.cnki.hsfwest.20231220004 |
| 分类号: |
| 基金项目:安徽省哲学社会科学规划项目（AHSKQ2021D132） |
|
| The optimization method for adaptive thermal comfort models in naturally ventilatedbuildings |
|
CAO Wen,WU Weidong
|
| Abstract: |
| Adaptive thermal comfort (ATC) models can be used to achieve energy savings by
dynamically specifying the room set point temperature. The accurate ATC models can assist in indoor
environmental control and building energy-saving design. Existing models mainly include adaptive
heat balance and adaptive regression models. Due to the complicated PMV index of adaptive heat
balance in practice often predicting worse than simple indices (temperatures), the adaptive regression
models (such as ASHRAE 55 and EN 16798 models) are more widely used. This study focuses on the
regression model, which is a regression equation with outdoor temperatures as the independent
variable and neutral temperatures as the dependent variable. The neutral temperature can be calculated
using the Griffiths method. The operative temperature is the representative value of the dependent
variable of the model, and there are many kinds of representative values of the independent variable
(outdoor temperatures). According to the outdoor test data, different processing methods (such as
natural daily mean, monthly mean, 7-day running mean, 15-day running mean, 21-day running mean,
and so on) result in different representative values of outdoor temperatures. The representative values
of the independent variable of the existing models are not uniform, and the models built with different
representative values vary greatly. How to determine the appropriate representative values to establish
the optimal model? Little research has been done on the above question. There are two traditional
methods for establishing ATC models. The first method is that the representative value of the
independent variable is directly adopted from existing studies, which has the drawback of not
verifying the applicability and effectiveness of the representative value. The second method is to
select a reasonable representative value based on the maximum coefficient of determination (R2) to
determine the optimal ATC model. The main defect of this method is that it does not consider the
estimation standard error (Se) of the equation. The purpose of establishing ATC models is to evaluate
and predict. Determining the optimal model should consider not only the degree of prediction (R2) but
also the prediction error (Se). Few studies have applied the Se index to determine the optimal ATC
model. In this paper, R2 and Se are used as evaluation indexes to determine the optimal ATC model.
Since the evaluation indexes are not unique, the optimal solution cannot be obtained using a simple
comparison. Multi-objective optimization analysis is required to determine the optimal ATC model.
The purpose of this study is as follows. A method to optimize the ATC model is proposed, and a yearround
thermal comfort survey of seven dormitory buildings in Bengbu, China, is used as an example
to implement and validate the proposed method. The research methods and process are as follows.
Firstly, the thermal comfort survey was carried out using simultaneous instrumental tests and
questionnaires. The buildings were all operated in natural ventilation mode during the survey. The
survey was conducted over one year, in which indoor and outdoor thermal parameters were measured.
The outdoor parameters included air temperature and relative humidity, and the testing instrument was
placed in a louver box 1.5 meters above the outdoor ground. The questionnaire mainly consists of two
parts: objective information and subjective feelings. Then, the literature was consulted to summarize
all the representative values of outdoor temperature, and ATC models were built for eachrepresentative value of outdoor temperature. A total of 17 kinds of models were obtained. In addition, classification ATC models were also developed separately
by gender and season, as ATC models are affected by gender and season. Afterward, a multi-objective optimization method (grey relational analysis) was used
to evaluate all ATC models and ultimately determine the optimal ATC model. Finally, the different models were compared. The optimal annual ATC model
obtained from the study was compared with the most internationally recognized ATC models (ASHRAE 55, EN 16798, and GB/T50785) to verify the accuracy
of the optimal model in this study. The classification ATC models were compared with the annual model to verify the accuracy of the classification model. The
following conclusions are drawn. The optimal ATC model of this study can explain 69.6% of the information in the measured data. In comparison with existing
international ATC models, it is found that the optimal ATC model determined in this study has the best prediction accuracy and stability, indirectly confirming
the effectiveness of the method proposed in the paper. The difference in predictive performance between the classification ATC model and the annual model is
significant. The classification model has better predictive performance than the annual model. Compared with the annual model, the prediction accuracy of ATC
models established by seasons improved by 7.7% (winter), 13.6% (summer), and 8.5% (transition season), respectively. This study enriches the adaptive
thermal comfort theory and provides a valuable reference for accurately establishing ATC models. |
| Key words: adaptive thermal comfort (ATC) model grey correlation neutral temperature natural ventilation buildings statistical analysis |
