Dataset for the statistical modelling of occupant behavior in mixed-mode office buildings considering the number of occupants and architectural features

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.研究目标：本研究旨在探究并对比建筑使用者（Occupants）人数与建筑特征（Architectural Features）对混合模式办公建筑（Mixed-mode Office Buildings）内使用者行为（含空调开启与开窗行为）的影响。 方法概述：本研究于巴西圣保罗州圣卡洛斯市开展了为期18个月的监测工作，以采集选定环境因子及目标行为的相关数据。对采集到的原始数据进行后处理后，构建统计模型以预测两类控制操作：开窗行为（Window Opening）与空调开启操作（Air-conditioning Activation）。该模型以环境数据、建筑特征、各办公间的建筑使用者人数作为输入变量，预测每一项控制操作的触发概率。 文件说明： Raw_RH_Temp：该Excel文件包含监测周期（2017年10月—2019年5月）内每月由圣卡洛斯气象站采集的全部室外气象变量数据工作表。本文件保留原始采集格式，未做任何修改，仅包含逐小时数据。为更全面表征当地气候特征，文件同时提供了温度与相对湿度（Relative Humidity, RH）的极值数据，但建模仅使用温度与RH的瞬时值。圣卡洛斯气象站仅提供逐小时原始数据。 Treated_Temp：该Excel文件包含2017年10月—2019年5月每月的处理后室外温度（摄氏度，℃）数据工作表。其中“I类工作表”存储原始数据，“II类工作表”为应用宏代码处理后的数据，可将原始数据规整为10分钟间隔的时间序列。 Treated_RH：该Excel文件包含2017年10月—2019年5月每月的室外相对湿度（RH，单位：%）数据工作表。其中“I列”存储原始数据，“II类工作表”为应用宏代码处理后的数据，可将原始数据规整为10分钟间隔的时间序列。 Treated Data_Offices： 该文件夹存储用于构建模型的处理后办公场所数据集。文件夹内包含与数据集中各办公间对应的子文件夹，每个子文件夹内的数据表对应该办公间的监测时段。建模所用数据均经过滤波处理：筛选出工作时段（每日8:30至18:00）的工作日数据，并排除法定假日及根据建筑使用者反馈确认的无人办公时段。 空调状态（Air Conditioning State, AC State）列中高亮的行代表计算结果存在误差，需根据图表中的空调温度数据进行手动修正。 建筑特征（Architectural Features）： 本文件包含从6个调研办公间采集的全部建筑特征参数，其中办公间编号、建筑使用者人数及窗户朝向（Window Orientation）均被纳入所构建的统计模型。