Research

Our BIOR lab aims at developing sustainable and scalable technologies and computational tools to make building and urban energy systems low-carbon, energy-efficient, energy-flexible, climate-resilient, and equitable. Our interdisciplinary research is at the interface of Building Environment and Energy Engineering (i.e., Architectural Engineering), Computer Science, and Control Engineering. We employ a multifaceted approach that encompasses data analytics & machine learning, physics-based modeling & simulation, optimization & model-based optimal controls, as well as experiments. These approaches have been deployed across a spectrum of scales, spanning from equipment- through building- and community- to city-scale.

 

Currently, our lab is focusing on:
  • Low-carbon building technologies for energy efficiency, flexibility, and climate resilience
  • Grid-interactive smart buildings and communities
  • Modelling, optimization, and optimal control of building, district, and urban energy systems
  • Modelling, optimization, and optimal control of HVAC&R systems
  • Data analytics and machine learning for buildings
  • Uncertainty analysis for buildings
  • Occupant behavior in buildings

Research Methods

Data Analytics

Data Cleaning | Data Visualization | Data Clustering | EDA

Data-driven Modelling

Grey-/Black-box | Machine Learning | Deep Learning

Physics-based Modelling

Building Thermal Dynamics | Refrigeration/HVAC systems | ODEs and State Space Representations

Computational Optimisation

Mathmatical and Metaheuristic (GA/PSO) Algorithms

Model Predictive Control (MPC)

Moving from predictive to prescriptive analytics | MPC = Data-driven Modeling + Receding-horizon Numerical Optimization + Online Feedback Control

Uncertainty Analysis

Monte Carlo technique |Stochastic Occupancy and Occupant Behavior (Markov Chain)