Formalized control logic fault definition with ontological reasoning for air handling units
Not Available
Lei, Xuechen↗
Engineering topics
Berges, Mario
Publications and source records attributed to Berges, Mario.
OccuTherm: Occupant Thermal Comfort Inference using Body Shape Information
This repository contains the official data from a USDOE-funded project at Carnegie Mellon University and Bosch Research Pittsburgh. The primary goal of the project was to investigate the relationship between indoor commercial building occupant thermal comfort and various biometric and environmental predictors. We performed 77 individual comfort experiments, approved by our Institutional Review Board (IRB) and in satisfaction of participant consent guidelines. Our goal was to generate a dataset than enables comprehensive study of human thermal comfort preferences, in a commercial building environment, across a wide range of indoor environmental conditions. The data is comprised of the following feature groups: depth camera frames, biometrics sensor data, body shape information, subjective comfort data from the mobile device application, environmental sensor data from the commercial building HVAC system, and outdoor weather station data.
47 OTHER INSTRUMENTATION↗
Dataset: Inferring Thermal Comfort using Body Shape Information Utilizing Depth Sensors
Thermal comfort is very important for well-being and productivity of building occupants. It has been shown that body shape is a useful feature to determine thermal comfort of individuals [2]. It is because, the heat dissipation rate of individuals depends on the body surface area. As a result, a tall and skinny person can tolerate higher room temperature than a rounded body shape person [5]. In order to test this hypothesis, we performed a year-long experiment in 2017, where we recruited 77 participants and put each of them in a thermally controlled conference room in CMU for 3 hours and recorded their subjective responses regarding thermal comfort at different temperature ranging from 60°F to 80°F. In addition, we collected depth data of individuals using a vertically mounted Microsoft Kinect for XBOX One at the entrance of the conference room to capture their body shape. We also collected biometric features (e.g., Galvanic Skin Response (GSR), skin temperature) using a Microsoft Health Band worn by the subjects. The resulting dataset provides rich information regarding how different features can be used to infer thermal comfort of the individuals.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
Dataset: Occupancy Detection, Tracking, and Estimation Using a Vertically Mounted Depth Sensor
Occupancy detection, tracking, and estimation has a wide range of applications including improving building energy efficiency, safety, and security of the occupants. As depth sensors are getting cheaper, they offer a viable solution to estimate occupancy accurately in a non-privacy invasive manner. Even though there are publicly available depth datasets, they do not consider placing the sensor in the ceiling looking downwards to estimate occupancy. We deployed four Kinect for XBOX One in four CMU classrooms and conference rooms for a period of four weeks in 2017 and collected over 6 TB of depth data. We annotate this huge dataset by labelling bounding boxes around occupants and release the annotated dataset.
47 OTHER INSTRUMENTATION↗