IDEAS Lab is a research group in the Department of Building 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.
Experiments and CFD studies for droplet and bioaerosol transmission inside building. A project funded by NUS COVID-19 Research Seed Grant.
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.
Building lifecycle automatic data segmentation using natural language processing
An R package for creating future weather files under climate changes for building energy simulation
Impacts of climate change on building energy systems
A framework for identifying and quantifying the impact of occupancy-related schedules on ECMs’ performance using 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). His research interest is in building energy modeling and simulation, focusing on model calibration and uncertainty quantification. 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 is also the subject editor (validation, calibration, and uncertainty) for the journal Building Simulation and an NUS principal investigator at the SinBerBEST program Theme E - Cyber-Physical Testbed.
PhD in Building Performance and Diagnostics, 2017
Carnegie Mellon University
Deep learning, Indoor air quality, Building modelling and simulation
Smart buildings and systems, Automation and controls, Machine learning and optimization, Data-driven modeling, User-centric design, IoT and sensing platforms, Human-machine interfaces
IoT Applications, Occupancy Detection, Smart Energy Management Systems, Data Analytics and Applied Machine Learning
Continual Learning, Physical-based Deep Learning
Computational Fluid Dynamics, Convective Heat transfer, Microclimate
Building Energy Simulation, Building Data Analytics, Model Predictive Control
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