Research

Research Overview

BIOR LOGO

Smart

Low-carbon

Energy-efficient

Demand-flexible

Climate-resilient

Equitable

Building, District, and Urban Energy Systems.

Our BIOR lab aims at developing sustainable and scalable technologies and computational tools to make building, district, and urban energy systems smart, low-carbon, energy-efficient, energy-flexible, climate-resilient, and equitable using optimization, learning, and control.

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

Mathematical 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→…