Dataset for the statistical modelling of occupant behavior in mixed-mode office buildings considering the number of occupants and architectural features
datasetposted on 16.05.2020 by Camila Grassi, Diego Zumpano, Pedro Henrique Silva Mattia, Karin Maria Soares Chvatal
Datasets usually provide raw data for analysis. This raw data often comes in spreadsheet form, but can be any collection of data, on which analysis can be performed.
Research objective: This research aimed to investigate and compare the influence of the number of occupants and architectural features on occupant behaviour, in relation to air-conditioning activation and window opening in mixed-mode office buildings. Method overview: The method consisted in an 18-month monitoring campaign in the city of São Carlos, SP, Brazil, to collect data on the selected environmental factors, as well as the studied behaviours. The collected data was post-processed and used to create statistical models to predict each action, window opening and air-conditioning activation. The models use environmental data and architectural features, as well as the number of users in each office, to predict the probability of use of each control. Files description: Raw_RH_Temp: This Excel file contains the sheets of all the data imported from the Meteorological Station of São Carlos in terms of outdoor variables for each month during the measuring campaign (October 2017 – May 2019). They are here presented without any changes (raw data), thus contain only the hourly data. Although the maximum and minimum values for temperature and relative humidity (RH) are also presented to better represent the local climate, only the instantaneous values (for temperature and RH) were used to develop the model. The Meteorological Station provides only the hourly data. Treated_Temp: This Excel file contains the sheets of the treated outdoor temperature (C°) for each month (October 2017 – May 2019). The “I” tables contain the raw data, while the “II” tables contain the data with the Macro code applied – which enabled the data to be fitted into the 10-minute interval. Treated_RH: This Excel file contains the sheets of the outdoor relative humidity (RH; in %) for each month (October 2017 – May 2019). The “I” columns contain the raw data, while the “II” tables contain the data with the Macro code applied - which enabled the data to be fitted into the 10-minute interval. Treated Data_Offices: The folder Treated Data_Offices contains the treated data set that was used to build the models. Within this folder there are folders for each of the offices within the data set. The tables within each folder are respective to the period of time when the monitoring campaign occurred in that office. The data used for the models were taken after filters were applied. These filters were meant to select hours of work (8:30a.m. to 6p.m.), weekdays, and excluded holidays and non-occupied days according to occupants’ reports. Lines highlighted in the AC State column indicate where the calculation was not accurate, and thus manually overwritten according to the AC Temperatures seen on the graphs. Architectural Features: This file contains all the architectural features collected from the six studied offices. The variables office ID, number of occupants and Window orientation were included in the statistical models created.