IDEAS Lab is a research group in the Department of the Built Environment in the College of Design and Engineering at the National University of Singapore (NUS) that engages in the integration building performance simulation with real-time data and machine learning. The group focuses on various aspects of building performance simulation including uncertainty quantification, model calibration, and building data analytics with the aim of creating energy efficient and healthier buildings.
Advanced machine learning based control for the HVAC systems
This study aims at improving the scalability of MPC by addressing modeling-related issues and establishing an automated framework.
An R package for creating future weather files under climate changes for building energy simulation
A framework for seamless integration between Building Energy Simulation (BES) and data-driven analytics.
Generating certified energy models in Singapore through an M&V framework.An NUS project in collaboration with Professor Godfried L. Augenbroe (Georgia Institute of Technology)
Dr. Adrian Chong is an Assistant Professor in the Department of the Built Environment at the National University of Singapore (NUS) and a Fellow of the International Building Performance Simulation Association (IBPSA). His research interest is is rooted in addressing the multi-faceted challenges of optimizing building energy efficiency and performance.At the core of this endeavor are his interest in model calibration, uncertainty quantification, and occupant-centric building controls. At NUS, he leads the Integrated Data, Energy Analysis + Simulation (IDEAS) lab, a multidisciplinary group researching the interaction between building performance simulation, measured data, and machine learning. Adrian also serves as a subject editor for the journal Building Simulation and holds the role of Early Career Board Member for the journal Building and Environment.
PhD in Building Performance and Diagnostics, 2017
Carnegie Mellon University
Deep learning, Indoor air quality, Building modelling and simulation
Building Energy Simulation, Building Data Analytics, Model Predictive Control
IoT Applications, Occupancy Detection, Smart Energy Management Systems, Data Analytics and Applied Machine Learning
Continual Learning, Physical-based Deep Learning
Building Energy Modeling, Building Data Analytic, Fault Detection & Diagnosis, Digital Twins
Computational Fluid Dynamics, Convective Heat transfer, Microclimate
Building Performance Modelling and Optimization
Building Energy Simulation, Bayesian Calibration, Data Analytics and Applied Machine Learning
Building Performance Modelling and Optimization, Model Prodictive Control (MPC), Indoor CFD Application, Data Analytics
Model calibration/validation, Building Energy Performance Simulation, Occupant behavior models, District energy demand prediction
Smart buildings and systems, Automation and controls, Machine learning and optimization, Data-driven modeling, User-centric design, IoT and sensing platforms, Human-machine interfaces