Clayton Miller

National University of Singapore | NUS · Department of the Built Environment

Simulation model calibration has been long identified as a key means of reconciling the consumption and efficiency characteristics of buildings. A key step in this process is the creation of the actual diversity factor profiles for occupancy and various energy end uses such as lighting, plug-loads, and HVAC. Creation of these model inputs is conven...

We present an approach for rapidly assessing the per-formance of early design stage building information models (BIM) from both the building and urban sys-tems scale. This effort builds upon two previously de-veloped tools, the Design Performance Viewer (DPV) and the CitySim urban simulation engine. It couples them to produce a more informed model....

The amount of sensor data generated by modern building systems is growing rapidly. Automatically discovering the structure of diurnal patterns in this data supports implementation of building commissioning, fault detection and retrofit analysis techniques. Additionally, these data are crucial to informing design professionals about the efficacy of...

Building retrofit analysis of buildings in Switzerland traditionally relies on expert heuristics and best practices. These processes are not often supplemented by data or model-driven techniques that would enhance the accuracy and ability to quantify the impact of innovative technologies. We present a process of calibrated building energy model (BE...

Building performance research using various informatics techniques has progressed extensively in the last twenty years by advancing the fields of automated fault detection and diagnostics (AFDD), commissioning, data mining, and visualization for commercial buildings. Despite this effort, it has been difficult to understand the effectiveness of diff...

Building energy prediction and management has become increasingly important in recent decades, driven by the growth of Internet of Things (IoT) devices and the availability of more energy data. However, energy data is often collected from multiple sources and can be incomplete or inconsistent, which can hinder accurate predictions and management of...

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 the occupied spaces need to be effectively used to facilitate meaningful interactions and create a positive, sustainable work culture. This work aims to determine when spon...

The building sector plays a crucial role in the worldwide decarbonization effort, accounting for significant portions of energy consumption and environmental effects. However, the scarcity of open data sources is a continuous challenge for built environment researchers and practitioners. Although several efforts have been made to consolidate existi...

Occupant-Centric Control and Operation (OCC) represents a transformative approach to building management, integrating sensing of indoor environmental quality, occupant presence, and occupant-building interactions. These data are then utilized to optimize both operational efficiency and occupant comfort. This paper summarizes the findings from the I...

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 various aspects. While most studies focus on the city and building scales, the rooftop observatory provides high temporal and spatial resolution observations with dynamic int...

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 work. The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and hybrid work arrangements. Many occupants and employers are considering keeping...

The psychrometric chart is the most common data visualization technique for the designers of thermal comfort systems worldwide. From its humble roots as means of expressing the characteristics of air in building systems design, the use of the chart has grown to include the representation of the zones of human thermal comfort according to both conve...

Conventional sidewalk studies focused on quantitative analysis of sidewalk walkability at a large scale which cannot capture the dynamic interactions between the environment and individual factors. Embracing the idea of Tech for Social Good, Urban Digital Twins seek AI-empowered approaches to bridge humans with digitally-mediated technologies to en...

Personal thermal comfort models are used to predict individual-level thermal comfort responses to inform design and control decisions of buildings to achieve optimal conditioning for improved comfort and energy efficiency. However, the development of data-driven thermal comfort models requires collecting a large amount of sensor-related measurement...

Building thermal modeling is the founding stone upon which numerous carbon reduction strategies in the building sector are built. Yet, as of today, little to no interpretable and calibrated models founded on real-world measurements have been open-sourced. This work attempts to remedy this deficiency and renders public improved results of a recentl...

Personal thermal comfort models are a paradigm shift in predicting how building occupants perceive their thermal environment. Previous work has critical limitations related to the length of the data collected and the diversity of spaces. This paper outlines a longitudinal field study comprising 20 participants who answered Right‐Here‐Right‐Now surv...

This paper presents a digital twin of a university campus in Singapore as a demonstrator for a digital-twin enabled approach to district energy resilience. This paper focuses mainly on the development of the building energy and occupancy models in the digital twin, which are complemented by a user interface for real-time data visualization and scen...

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 infrared (IR) observatory. To achieve this first, an image processing scheme, for cleaning and extraction of the temperature time series from the thermal images is implemented....

People spend the majority of their time indoors and environmental conditions affect their perceptions, performance, health, and well-being. Buildings should, therefore, be designed and operated with the main objective of providing comfortable environments for occupants and meeting their needs. However, in practice, occupants' perceptions and sensat...

This paper studies heat fluxes from contributors and mitigators of urban heat islands using thermal images and weather data. Thermal images were collected from an observatory operating on the rooftop of a building between November 2021 and April 2022. Over the same period, an automatic weather station network was used to measure weather conditions...

Cohort Comfort Models (CCM) are introduced as a technique for creating a personalized thermal prediction for a new building occupant without the need to collect large amounts of individual comfort-related data. This approach leverages historical data collected from a sample population, who have some underlying preference similarity to the new occup...

Cities today encounter significant challenges pertaining to urbanization and population growth, resource availability, and climate change. Concurrently, unparalleled datasets are generated through Internet of Things (IoT) sensing implemented at urban, building, and personal scales that serve as a potential tool for understanding and overcoming thes...

The perception, physiology, behavior, and performance of building occupants are influenced by multi-domain exposures: the simultaneous presence of multiple environmental stimuli, i.e., visual, thermal, acoustic, and air quality. Despite being extensive, the literature on multi-domain exposures presents heterogeneous methodological approaches and in...

The paper presents a review on major contributions in infrared thermography to study the built environment at multiple scales. To elaborate the review, hundreds of studies conducted between the 1980s and 2020s were first selected based on their relevance to the scope. Afterward, the most relevant contributions were classified and chronologically so...

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 work. The global COVID-19 pandemic accelerated this phenomenon rapidly through lockdowns and hybrid work arrangements. Many occupants and employers are considering keeping...

We introduce Cohort Comfort Models, a new framework for predicting how new occupants would perceive their thermal environment. Cohort Comfort Models leverage historical data collected from a sample population, who have some underlying preference similarity, to predict thermal preference responses of new occupants. Our framework is capable of exploi...

A thermohygrometer is an instrument that is able to measure relative humidity and air temperature, which are two of the fundamental parameters to estimate human thermal comfort. To date, the market offers small and low-cost solutions for this instrument, providing the opportunity to bring electronics closer to the end-user and contributing to the p...

Research is needed to explore the limitations and potential for improvement of machine learning for building energy prediction. With this aim, the ASHRAE Great Energy Predictor III (GEPIII) Kaggle competition was launched in 2019. This effort was the largest building energy meter machine learning competition of its kind, with 4,370 participants who...

District-scale energy demand models can be powerful tools for understanding interactions in complex urban areas and optimising energy systems in new developments. The process of coupling characteristics of urban environments with simulation software to achieve accurate results is nascent. We developed a digital twin through a web map application fo...

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) infectious disease (COVID-19) risk perception, ii) privacy and distraction in an office context, and iii) triggers of various movement-related behaviors in buildings. Thi...

Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning competition used an extensive meter data set to crowdsource the most accurate machine learning workflow for whole buildin...

To reach the carbon emission reduction targets set by the European Union, the building sector has embraced multiple strategies such as building retrofit, demand side management, model predictive control and building load forecasting. All of which require knowledge of the building dynamics in order to effectively perform. However, the scaling-up of...

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is th...

Data-driven building energy prediction is an integral part of the process for measurement and verification, building benchmarking, and building-to-grid interaction. The ASHRAE Great Energy Predictor III (GEPIII) machine learning competition used an extensive meter data set to crowdsource the most accurate machine learning workflow for whole buildin...

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 of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each buildin...

Decarbonizing the building stock is a central component of global climate change mitigation efforts. In practice, this decarbonization can be achieved by a variety of different measures, including improvements in building energy efficiency, electrification of energy demand to reduce reliance on fossil fuels, and installation of distributed (renewab...

Collecting intensive longitudinal thermal preference data from building occupants is emerging as an innovative means of characterizing the performance of buildings and the people who use them. These techniques have occupants giving subjective feedback using smartphones or smartwatches frequently over the course of days or weeks. The intention is th...

The ASHRAE Great Energy Predictor III (GEPIII) competition was held in late 2019 as one of the largest machine learning competitions ever held focused on building performance. It was hosted on the Kaggle platform and resulted in 39,402 prediction submissions, with the top five teams splitting $25,000 in prize money. This paper outlines lessons lear...

Building energy use benchmarking is the process of measuring the energy performance of buildings, relative to their peer group, for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The peer groups...

The rapid growth of machine learning (black-box) techniques and computing capacity has started to transform many research domains, including building performance analysis. However, physical interpretation of these models remains a challenge due to their opaque nature. This paper outlines an experiment to unveil analytical expressions from an open-s...

Thermal comfort affects the well-being, productivity, and overall satisfaction of building occupants. However, due to economical and practical limitations, the number of longitudinal studies that have been conducted is limited, and only a few of these studies have shared their data publicly. Longitudinal datasets collected indoors are a valuable re...

Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person’s thermal preference. The spatial context of a building can provide information to models about the windows, wa...

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 of the variables in energy prediction models, occupant behavior, is crucial for prediction performance but hard-to-measure or time-consuming to collect from each buildin...

Conventional thermal preference prediction in buildings has limitations due to the difficulty in capturing all environmental and personal factors. New model features can improve the ability of a machine learning model to classify a person's thermal preference. The spatial context of a building can provide information to models about the windows, wa...