Integrated Data, Energy Analysis + Simulation
A Passion for Better Buildings
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.
Also, check out our two sibling labs: the Building and Urban Data Science (BUDS) Lab and the Urban Analytics Lab (UAL), with whom we collaborate closely within NUS. .
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). His research interest is in building energy modeling and simulation, focusing on building energy efficiency, model calibration, and occupant-centric building operation. 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, an Early Career Board Member for the journal Building and Environment, and an NUS principal investigator at the SinBerBEST program Theme E - Cyber-Physical Testbed.
PhD in Building Performance and Diagnostics, 2017
Carnegie Mellon University
Model calibration/validation, Building Energy Performance Simulation, Occupant behavior models, District energy demand prediction
Deep learning, Indoor air quality, Building modelling and simulation
Building Energy Simulation, Building Data Analytics, Model Predictive Control
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
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