264 citations · 1.1k across the 18 of their papers we have counts for
18 papers
BIM-to-BRICK: Using graph modeling for IoT/BMS and spatial semantic data interoperability within digital data models of buildings
Filippo Vittori, Chuan Fu Tan, Anna Laura Pisello +2
The holistic management of a building requires data from heterogeneous sources such as building management systems (BMS), Internet-of-Things (IoT) sensor networks, and building inf…
Utilizing wearable technology to characterize and facilitate occupant collaborations in flexible workspaces
Kristi Maisha, Mario Frei, Matias Quintana +3
Hybrid working strategies have become, and will continue to be, the norm for many offices. This raises two considerations: newly unoccupied spaces needlessly consume energy, and th…
District-scale surface temperatures generated from high-resolution longitudinal thermal infrared images
Subin Lin, Vasantha Ramani, Miguel Martin +6
The paper describes a dataset that was collected by infrared thermography, which is a non-contact, non-intrusive technique to collect data and analyze the built environment in vari…
Longitudinal thermal imaging for scalable non-residential HVAC and occupant behaviour characterization
Vasantha Ramani, Miguel Martin, Pandarasamy Arjunan +3
This work presents a study on the characterization of the air-conditioning (AC) usage pattern of non-residential buildings from thermal images collected from an urban-scale infrare…
Cozie Apple: An iOS mobile and smartwatch application for environmental quality satisfaction and physiological data collection
Federico Tartarini, Mario Frei, Stefano Schiavon +2
Collecting feedback from people in indoor and outdoor environments is traditionally challenging and complex in a reliable, longitudinal, and non-intrusive way. This paper introduce…
Elastic buildings: Calibrated district-scale simulation of occupant-flexible campus operation for hybrid work optimization
Martín Mosteiro-Romero, Clayton Miller, Adrian Chong +1
Before 2020, the way occupants utilized the built environment had been changing slowly towards scenarios in which occupants have more choice and flexibility in where and how they w…
Smartwatch-based ecological momentary assessments for occupant wellness and privacy in buildings
Clayton Miller, Renee Christensen, Jin Kai Leong +5
This paper describes the adaptation of an open-source ecological momentary assessment smart-watch platform with three sets of micro-survey wellness-related questions focused on i)…
Cohort comfort models -- Using occupants' similarity to predict personal thermal preference with less data
Matias Quintana, Stefano Schiavon, Federico Tartarini +2
We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collec…
Targeting occupant feedback using digital twins: Adaptive spatial-temporal thermal preference sampling to optimize personal comfort models
Mahmoud Abdelrahman, Clayton Miller
Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people w…
Using Google Trends as a proxy for occupant behavior to predict building energy consumption
Chun Fu, Clayton Miller
In recent years, the availability of larger amounts of energy data and advanced machine learning algorithms has created a surge in building energy prediction research. However, one…
Personal thermal comfort models using digital twins: Preference prediction with BIM-extracted spatial-temporal proximity data from Build2Vec
Mahmoud Abdelrahman, Adrian Chong, Clayton Miller
Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve th…
Balancing thermal comfort datasets: We GAN, but should we?
Matias Quintana, Stefano Schiavon, Kwok Wai Tham +1
Thermal comfort assessment for the built environment has become more available to analysts and researchers due to the proliferation of sensors and subjective feedback methods. Thes…
Humans-as-a-sensor for buildings: Intensive longitudinal indoor comfort models
Prageeth Jayathissa, Matias Quintana, Mahmoud Abdelrahman +1
Evaluating and optimising human comfort within the built environment is challenging due to the large number of physiological, psychological and environmental variables that affect…
The ASHRAE Great Energy Predictor III competition: Overview and results
Clayton Miller, Pandarasamy Arjunan, Anjukan Kathirgamanathan +8
In late 2019, ASHRAE hosted the Great Energy Predictor III (GEPIII) machine learning competition on the Kaggle platform. This launch marked the third energy prediction competition…
Build2Vec: Building Representation in Vector Space
Mahmoud Abdelrahman, Adrian Chong, Clayton Miller
In this paper, we represent a methodology of a graph embeddings algorithm that is used to transform labeled property graphs obtained from a Building Information Model (BIM). Indust…
Spacematch: Using environmental preferences to match occupants to suitable activity-based workspaces
Tapeesh Sood, Patrick Janssen, Clayton Miller
The activity-based workspace (ABW) paradigm is becoming more popular in commercial office spaces. In this strategy, occupants are given a choice of spaces to do their work and pers…
The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition
Clayton Miller, Anjukan Kathirgamanathan, Bianca Picchetti +7
This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 meas…
EnergyStar++: Towards more accurate and explanatory building energy benchmarking
Pandarasamy Arjunan, Kameshwar Poolla, Clayton Miller
Building energy performance benchmarking has been adopted widely in the USA and Canada through the Energy Star Portfolio Manager platform. Building operations and energy management…