README.md

CLIMATEREADY 'Thermal Comfort Survey Amid Heatwaves' Dataset

This repository gives you access to the CLIMATEREADY survey dataset containing thermal comfort votes during the 2021 and 2022 heatwave periods in Pamplona, Spain, as well as other relevant parameters self-reported by surveyees (e.g. occupant characteristics and behaviour, key building/dwelling characteristics, sleep problems, heat-related symptoms), used as case study for the research paper Exploring indoor thermal comfort and its causes and consequences amid heatwaves in a Southern European city—An unsupervised learning approach, accepted and published in Building & Environment Journal.

This dataset is part of the CLIMATEREADY research project. The aim of this project is the assessment of the adaptability of residential dwellings in Spain to the global warming, promoting passive measures in the design and use of the buildings to get an adequate indoor environment in summer conditions, minimising and quantifying overheating risks and with the minimum cooling.

Column names and description

parameter	description
ID	Unique dwelling identifier [integer]
Timestamp	Data collection timestamp [dd/mm/yyy hh:mm]
Date	Data collection date [YYMMDD]
Hour	Data collection hour [hh]
hw_True	Hot weather condition indicator [boolean, True = 1]
meanTout	Mean outdoor temperature [ºC, float]
ThermostatTemp	Thermostat temperature setting [ºC, float]
TSen_day	Temperature sensation during the day, on the ASHRAE scale [-3 (cold) to +3 (hot), integer]
TSen_night	Temperature sensation during the night, on the ASHRAE scale [-3 (cold) to +3 (hot), integer]
TSatisf_day	Temperature satisfaction during the day, on the ASHRAE scale [-3 (highly unsatisfied) to +3 (highly satisfied), integer]
TSatisf_night	Temperature satisfaction during the night, on the ASHRAE scale [-3 (highly unsatisfied) to +3 (highly satisfied), integer]
TPref_day	Temperature preference during the day, on the ASHRAE scale [-1 (prefer cooler) to +1 (prefer warmer), integer]
TPref_night	Temperature preference during the night, on the ASHRAE scale [-1 (prefer cooler) to +1 (prefer warmer, integer)]
Gender	Respondent gender [boolean, Woman = 1]
Age	Respondent age [integer]
NatVent_night	Nighttime natural ventilation usage [boolean, True = 1]
UsesCoolingAlternatives	Use of alternative cooling (e.g. portable fans) [boolean, True = 1]
Hour_7_14h_True	Activity indicator from 7 AM to 2 PM [boolean, True = 1]
Hour_14_21h_True	Activity indicator from 2 PM to 9 PM [boolean, True = 1]
Hour_21_7h_True	Activity indicator from 9 PM to 7 AM [boolean, True = 1]
ShadingDevices_No	No use of shading devices [boolean, True = 1]
ShadingDevices_WhenDirectSun	Use shading devices when in direct sun [boolean, True = 1]
ShadingDevices_AllAfternoon	Use shading devices all afternoon [boolean, True = 1]
ShadingDevices_AllMorning	Use shading devices all morning [boolean, True = 1]
ShadingDevices_AllDay	Use shading devices all day [boolean, True = 1]
NatVent_day_ToutCool	Use natural ventilation during the day when outside is cooler [boolean, True = 1]
NatVent_day_Anytime	Use natural ventilation during the day at any time [boolean, True = 1]
NatVent_day_No	No daytime natural ventilation [boolean, True = 1]
hasAC_No	No air conditioning [boolean, True = 1]
hasAC_DormOrLiving	Air conditioning in dormitory or living area [boolean, True = 1]
hasAC_DormAndLiving	Air conditioning in both dormitory and living area [boolean, True = 1]
hasAC_AllRooms	Air conditioning in all rooms [boolean, True = 1]
HouseholdSize	Number of people in household [integer, True = 1]
is_31001	Is in postal code 31001 [boolean, True = 1]
is_31002	Is in postal code 31002 [boolean, True = 1]
is_31003	Is in postal code 31003 [boolean, True = 1]
is_31004	Is in postal code 31004 [boolean, True = 1]
is_31005	Is in postal code 31005 [boolean, True = 1]
is_31006	Is in postal code 31006 [boolean, True = 1]
is_31007	Is in postal code 31007 [boolean, True = 1]
is_31008	Is in postal code 31008 [boolean, True = 1]
is_31009	Is in postal code 31009 [boolean, True = 1]
is_31010	Is in postal code 31010 [boolean, True = 1]
is_31011	Is in postal code 31011 [boolean, True = 1]
is_31012	Is in postal code 31012 [boolean, True = 1]
is_31013	Is in postal code 31013 [boolean, True = 1]
is_31014	Is in postal code 31014 [boolean, True = 1]
is_31015	Is in postal code 31015 [boolean, True = 1]
is_31016	Is in postal code 31016 [boolean, True = 1]
isNot_pamplona	Is not in Pamplona [boolean, True = 1]
before_1980	Building built before 1980 [boolean, True = 1]
between_1980_2006	Building built between 1980 and 2006 [boolean, True = 1]
after_2007	Building built after 2007 [boolean, True = 1]
dwelling_OldTown	Located in old town [boolean, True = 1]
dwelling_Block	Located in a block [boolean, True = 1]
dwelling_Tower	Located in a tower [boolean, True = 1]
dwelling_Detached	Single-family dwelling [boolean, True = 1]
dwelling_Other	Other types of dwelling [boolean, True = 1]
Rehab_No	No retrofitting, or only structural or accessibility retrofitting [boolean, True = 1]
Rehab_Yes	Energy retrofitting: wall and/or roof insulation, window replacement [boolean, True = 1]
Income_below1500	Monthly income below 1500 [boolean, True = 1]
Income_between_1500_3500	Monthly income between 1500 and 3500 [boolean, True = 1]
Income_above_3500	Monthly income above 3500 [boolean, True = 1]
Occ_NormallyAtHome	Normally at home [boolean, True = 1]
Occ_NotAlwaysAtHome	Not always at home [boolean, True = 1]
SrfcArea_below90	Surface area below 90 sqm [boolean, True = 1]
Storey_UpperFloor	Upper floor dwelling [boolean, True = 1]
Storey_NotApartment	Not an apartment but a single-family dwelling [boolean], True = 1
numOrient_1	Dwelling has only one orientation [boolean, True = 1]
numOrient_above2	Dwelling has more than two orientations [boolean, True = 1]
LivRoom_SrfcAreaWindow_below2	Living room window surface area below 2 sqm [boolean, True = 1]
LivRoom_SrfcAreaWindow_above2	Living room window surface area above 2 sqm [boolean, True = 1]
Bedroom_SrfcAreaWindow_below2	Bedroom window surface area below 2 sqm [boolean, True = 1]
Bedroom_SrfcAreaWindow_above2	Bedroom window surface area above 2 sqm [boolean, True = 1]
AC_Installed_Yes	Dwelling has air conditioning installed [boolean, True = 1]
WouldYouInstallAC_Yes	Would install AC? [boolean, True = 1]
hasCoolRoom	Has 'cool' room (i.e. 'cool' retreat) [boolean, True = 1]
hasCoolingAlternatives	Occupant has cooling alternatives (e.g. portable fans) [boolean, True = 1]
HeatSymptoms	Occupant has heat symptoms during the day (e.g. dizziness or low blood pressure, headache, palpitations, hyperventilation) [boolean, True = 1]
SleepingProblems	Occupant reports having sleep problems due to heat [boolean, True = 1]

Running the Python code within Google Colab

The unsupervised learning techniques were implemented using Scikit-Learn version 3.2.1

Running the code in R

Use the csv files R_logistic_hclust and R_logistic_kmeans to run the code in R. You can find the code in Github. You need to set your personal working directory (setwd).

Preview HTML file

Download the climateready.html file, and once downloaded, open it with your default PC browser.

Please cite us if you use this dataset

Gamero-Salinas J., López-Hernández D., González-Martínez P., Arriazu-Ramos A., Monge-Barrio A., Sánchez-Ostiz A. (2024). Exploring indoor thermal comfort and its causes and consequences amid heatwaves in a Southern European city—An unsupervised learning approach, Building and Environment, 265. DOI: https://doi.org/10.1016/j.buildenv.2024.111986