Development of the ASHRAE Global Thermal Comfort Database II

Introduction

In the late 1990s, the ASHRAE Thermal Comfort Database I was created with the sole purpose of testing the adaptive temperature comfort hypothesis and creating a model. The resulting model became the empirical basis for ASHRAE’s adaptive thermal comfort standard. This project gathered high-quality, instrumental measurements of indoor temperatures and their simultaneous subjective thermal comfort assessments from 52 field studies that were conducted in 160 buildings around the world, most commercial offices. This database gathered almost all the available scientifically rigorous field study datasets (roughly 22,000 questionnaire responses and accompanying instrumental measurements) into one repository. The database was made available to the worldwide thermal comfort research community via internet access after the completion of the original ASHRAE project.

Inductive strategies that start with existing data and work "backward” towards a research question complement the traditional deductive model of science, which is based on hypotheses derived from theory and tested with experimental data. data mining research techniques have been used to benefit even the niche of thermal comfort research. The ASHRAE Thermal Comfort Database was created in 1992. It has been used for many research questions over the years. Refer to. Refs. Refer to Ref. ASHRAE Thermal Comfort Database I is the first place to go when you have questions about HVAC or thermal comfort. The current provisions in ASHRAE Standard 55 for elevated airspeed were based solely on Database I. As was the dynamic clothing model in the current ASHRAE Standard 55 that estimates indoor clothing insulation levels starting at 6:00 AM outside meteorological observations. The strong connection between thermal comfort and energy consumption in the built environment

Each submission was subject to rigorous quality control. The field data were organized in separate folders according to their origins. This included the name of the contributor, country, and sample size. Section contains a detailed listing of contributors as well as the size of each submission. Each folder contained the raw data files and supplementary codebook. Additionally, the publication(s), which provide details about the field study, such as geographical location, building type, and cooling strategy, season, climate, and other information, were included in each folder. These references can be found in the Comfort Database online query Builder interface and the visualization tool (more details are below). The meta-file was created by the research team to allow for easy filtering. It also included information such as the origin and characteristics.

Name for the contributor.

Publications (Authors and Title, Journal/Conference Information).

Year at the measurement.

Country.

City.

Season at the time of measurement.

Climate zone: Data were classified using the Koppen classification. The Results section provides a detailed description of how the samples were grouped into different climate categories.

Building type Data were divided into five categories: Multifamily housing and Office, Classroom, Senior Center, and Office.

Cooling strategy Data were used to identify the cooling strategy of the building. They also described the type of ventilation that was used during the study.

Sample for each contribution.

Directory - The path to the file where the codebook, raw data, and publication(s were) were saved.

Each field has been investigated for both objective and subjective variables of thermal comfort.

Since the Database I launched twenty years ago, new thermal comfort research has increased dramatically. So it makes sense to consolidate these new data into an even bigger repository. Comfort researchers will have more data to draw on to be able to dig down deeper and still deliver statistically significant results. As air-conditioning becomes a more common building control strategy, it should be possible for comfort researchers to see trends in thermal comfort preferences over longer periods. This paper documents the development, origin, content, accessibility, and accessibility to ASHRAE Global Thermal Comfort Database II (short: Comfort Database).

Methods

The team developed the data collection method based on the following requirements to ensure the database's quality would allow end-users to perform robust hypothesis testing.

The data had to be from field experiments and not climate chamber research. It needed to represent research done in "real", occupied buildings by "real" people, and not paid college students sitting in controlled indoor environments of climate chambers.

It was necessary to record both instrumental (indoor climate) and subjective data (questionnaire), in the same place.

Instead of the published or processed findings, the raw data files needed to be used to build the database.

To allow data harmonization with standard data formatting within the database, the raw data must be accompanied by a codebook.

Data must be published in either a conference paper or a peer-reviewed journal.

The database file was created by the research team using a standard spreadsheet format. The header included the unique identifier (i.e. variable names) for each column of data. The following categories were created from the information:

Basic identifiers such as building code and geographical location, year of measurements, heating/cooling strategies, and year of measurement.

Personal information regarding the field subjects, including sex, weight, height, and age.

Subjective thermal discomfort questionnaire, including sensation, acceptability, and preference as well as self-assessed metabolism rate (met), and clothing intrinsic thermal Insulation level(clo).

Instrumental Indoor Climate, including various temperatures, air velocity, and relative humidity.

Comfort indices include Predicted Mean Vote, Predicted Percentage Dissatisfied, and Standard Effective Temperature (SET). These were calculated uniformly across the entire database using a calculator fully compliant to the ISO Standard 7730 (2005) source code for PMV and PPD calculations and ASHRAE/ANSI Standard 56 (2017) source code for the 2-node SET Index. The calculator's compliance was verified by applying it to validation datasets provided in the appendices.

Indoor environmental controls are available (blinds and fans, operable windows, doors, heaters).

Information about the outdoor meteorological, including monthly average temperatures. Some data submissions included relevant meteorological data. Fields meteorological data were updated in the absence of those data using archival weather data from weather station websites. This data is based on information about the location and time of the measurements.

Before being included in the database, all datasets from individual studies had to pass a rigorous quality control process. To prevent any transmission errors, the research team validated each dataset by first comparing it with its related publication. To ensure that the records in the database were accurate, systematic quality control was carried out on each study. To identify anomalies in the data, we first visualized the distributions of each variable. Cross-plots were then made between variables (e.g. To check for errors in the data, cross-plots between two variables (e.g. thermal sensation and thermal comfort), were performed. A few rows were randomly chosen from each study to check for consistency between the original data and the standardized one. Because the data was derived from multiple studies, not all records included all the variables. In the absence of data, those cells were filled with null values. To remove data anomalies, the thermal comfort visualization tool was used (described later). In the Results section, you will find a detailed list of project identifiers as well as thermal comfort variables.

The database structure is such that the rows (or "records") represent the individual's responses to the questionnaire, while the columns contain the instrumental measurements, thermal index value, and outdoor meteorological observations. This summarizes the complete list of variables and their coding conventions in the database file. Each record can contain 49 thermal comfort variables. Each record has 65 columns. This allows you to express quantities in imperial or metric units. Any post-processed variables may also be flagged. The codebook for each parameter is included in the "offline" spreadsheet version. In the database is also the full citation of the original publication for each dataset. The University of California's DASH repository allows users to download the most recent version of the database.

Sources:

https://ro.uow.edu.au/cgi/viewcontent.cgi?article=2761&context=eispapers1

https://core.ac.uk/download/212718440.pdf