Dataset for "The effects of thermal mass and air-conditioning on summer temperature thermal comfort and occupant behaviour in homes"

The data collected during the longitudinal monitoring and survey campaign of the indoor environment, thermal comfort and occupant adaptive behavior during the summer season in Italian residential settings (Csa climate, Catania city). The campaign was completed in the period between 07/06/2019 and 19/09/2019, which includes the five heat health warnings and two heatwaves that occurred in the summer of 2019 in Catania, Italy.

The data collected and deposited here was used for the PhD thesis of Elisabetta Maria Patane': The effects of thermal mass and air-conditioning on summer temperature thermal comfort and occupant behaviour in homes.

Keywords:
Thermal comfort, Occupant behaviour, Temperature, Thermal mass, Air-conditioning, Homes, Summer

Cite this dataset as:
Patane', E., 2021. Dataset for "The effects of thermal mass and air-conditioning on summer temperature thermal comfort and occupant behaviour in homes". Bath: University of Bath Research Data Archive. Available from: https://doi.org/10.15125/BATH-01077.

Export

[QR code for this page]

Data

Airconditioning … Temperature.csv
text/plain (32MB)
Creative Commons: Attribution 4.0

Time-series of air-conditioning sub-hourly outlet temperature and derived on/off status in 18 air-conditioners. The air-conditioning name is defined by: "Cooling strategy_room level"-"Heavy Weight (TM2) or Medium Weight (TM1) constructions".

IndoorAirTemperature.csv
text/plain (2MB)
Creative Commons: Attribution 4.0

Time-series of hourly outdoor and indoor air temperature for each of the 100 rooms monitored from April to September 2019. The column name is defined by: "Cooling strategy_room level"-"Heavy Weight (TM2) or Medium Weight (TM1) constructions"-"House unit"-"Room".

ThermalComforta … viourSurvey.csv
text/plain (681kB)
Creative Commons: Attribution 4.0

Over 1330 spot measurements of indoor air temperature, relative humidity, velocity, thermal comfort and occupant adaptive behavior collected during summer 2019 in homes.

Creators

Elisabetta Maria Patane'
University of Bath

Contributors

David Coley
Supervisor
University of Bath

Marika Vellei
Supervisor
University of La Rochelle

University of Bath
Rights Holder

Documentation

Data collection method:

Longitudinal monitoring for air temperature, relative humidity, occupancy, and window and air-conditioning usage were employed. In addition, spot measurements were used to record air temperature, globe temperature, air velocity and relative humidity used for the thermal comfort analysis. All measurements were completed in the period between 07/06/2019 and 19/09/2019. The indoor dry bulb temperature (Ta, °C) and relative humidity (RH, %) were measured every 20 minutes with IButtons sensors. The sensors were placed in at least one living room and bedroom per flat and for households with high number of occupants, additional rooms were included such as a second or third bedrooms a dining, living and studio rooms. The air conditioning outlet temperature was recorded by IButton sensor, sampling every 20 minutes. Two units were targeted per home, one in the living-room or kitchen and the other one in the bedroom if available. It was placed on the horizontal louvre of the unit. The occupancy was recorded in 12 homes via HC-SR501 PIR infrared motion sensors, sampling each 5 seconds. The window opening state was recorded in 24 windows in 12 flats, for 6 days each, from the 25/08/2019 to the 19/09/2019. 8 state sensors of the HOBO UX90-001 type were employed. The state sensors were installed on openings that 62 inhabitants used most often when ventilating the dwelling. In order to monitor as many windows as possible, the 8 sensors were moved every 7 days from one flat to another one. The preferred windows were those in living or dining rooms and bedrooms. The spot measurements were carried out with 8 heat stress meters; Extech HT30 and HT200 models; and the Testo 0560 4053 Stick Thermo-anemometer. The measurements were taken from 15 to 25 minutes while the occupants filled out the questionnaire in the room. The heat-stress meters were provided to eight families to undertake the measurements by themselves. The questionnaire included the protocol of measurements translated in Italian on the first page. Thermal comfort and occupant behavior were monitored with a questionnaire administrated by the researcher. The procedure for this consisted of each occupant filling in the questions in a short time frame. All the questions were designed to be completed in 5 minutes in order to reduce the risk of participants leaving the study because of fatigue. The total number of questions is twenty-three, they are divided according to: contextual variables, thermal comfort votes, windows, shading and air-conditioning usage, spot measurements of indoor air temperature, relative humidity, mean radiant temperature and air velocity. All instruments and measurements protocol were carried out in agreement with the following standards: UNI EN ISO 7726:2001, UNI EN ISO 7730:2005. The thermal comfort survey are based on the 7-point scale thermal sensation votes, 5-points of thermal preference, 2-points of thermal acceptability in ASHRAE 55-2020UNI and EN ISO 16798:2019.

Data processing and preparation activities:

The first step was to create one file for each room; therefore, the different time-series files were concatenated according to the datetime index and the type of data. The second step was to find the outliers in the temperature datasets by checking the maximum and minimum values in each room. Then, two datasets for indoor room environmental conditions were created, one with a sub-hourly time-step which is used to record the state of air-conditioning, and another one with an hourly time-step which was created by resampling the readings and taking the mean of the values. The air-conditioning unit time-series were further manipulated in order to assign the “state” of the machine: switched on (1) or off (0). The difference between two consecutives sub-hourly outlet temperature readings was computed. It was assumed that if the difference was more than ± 5 °C, the air-conditioning was switched on or off. Whenever available, the self-reported data was also used to validate the air-conditioning status. The window state and the occupancy recordings were reported as collection of timestamps of the new position and occupant movement. The final data-set was generated for each variable by concatenating each room as a column and using the same time-series. The answers to the thermal comfort section of the questionnaire were translated into categorical variables according to the ANSI/ASHRAE Standard 55-2020. The metabolic rate (MET) and clothing insulation values (Icl) were estimated using the “Table 5.2.1.2 Metabolic Rates for Typical Tasks” and “Table 5.2.2.2.A - Clothing Insulation Icl Values for Typical Ensembles” in the ASHRAE 55 standard. The position of the window and shadings is either open or closed, and the status of the air-conditioning unit is switched on or off. The open and on cases are recorded as 1 in the excel sheet and closed or off as 0. The duration is expressed as the number of hours and minutes since the last state or position change of the system. This information was recorded as it was in the excel sheet. It is important to stress that the questionnaires reported local time reference. The precision of the self-reported duration of the state was assumed to be higher whenever the reference was less than 24 hours; in other cases, it was created a single category for duration which specifies a minimum of a day of duration of the current state. The “trigger” or “driver” is the reason behind the occupant choice for the concurrent position or state of the window, blind and AC systems. This is an open ended question because the answer could involve any number of factors, or unforeseen events can occur.

Documentation Files

readme.pdf
application/pdf (125kB)
Creative Commons: Attribution 4.0

1 page description of the content in each dataset, labels used, standard-compliance, issued heat health advisory and heatwave periods, instruments used.

Funders

Publication details

Publication date: 1 October 2021
by: University of Bath

Version: 1

DOI: https://doi.org/10.15125/BATH-01077

URL for this record: https://researchdata.bath.ac.uk/id/eprint/1077

Contact information

Please contact the Research Data Service in the first instance for all matters concerning this item.

Contact person: Elisabetta Maria Patane'

Departments:

Faculty of Engineering & Design
Architecture & Civil Engineering