Research

Research Overview

BIOR LOGO

Smart

Low-carbon

Energy-efficient

Demand-flexible

Climate-resilient

Equitable

Building and Urban Energy Systems.

Our BIOR lab aims at developing sustainable and scalable technologies and computational tools to make building and urban energy systems smart, 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.

Research Thrusts

Thrust 1: Optimal Design & Operation of Energy-Efficient, Demand-Flexible, and Climate-Resilient Buildings and Communities

Complex Cyber-Physical District Energy System [Link][Link]

Coordination and Negotiation in a Cyber-Physical Multi-Entity Residential Microgrid [Link]

Thrust 2: Stochastic Modeling, Estimation, Optimization, and Control of HVAC&R, Building, and Urban Energy Systems Considering Uncertainties

Schematic diagram of MPC for building energy systems [Link][Link]

Thrust 3: Explainable AI (XAI) for Smart Built Environments, including AI for Building Engineering and AI for Building Science

Explainable Machine Learning using Large-Scale Smart Meter Data [Link][Link][Link]

Thrust 4: Urban Building Energy Modeling for Large-Scale Decarbonization Assessment

Quantifying Uncertainty in Aggregate Energy Use and Demand Flexibility of Building Clusters Considering Stochastic Occupancy [Link]

Research Methodology

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)

MPC of Building Energy Systems

For energy efficiency/energy flexibility

More info

How MPC works?

Receding Horizon Optimisation: Opt.→Decision→Measurement→Opt.→Decision→Measurement→…