Publications

As a popular emission reduction tool, the carbon emission trading scheme (ETS) can potentially add an economic incentive for building owners to retrofit buildings in addition to the cost savings in energy. As a part of a comprehensive research project funded by US National Science Foundation, the Science Foundation Ireland,...

It is challenging to provide excellent indoor air environment, while saving operational cost and reducing CO 2 emission. In this work, we explored the tradeoffs among indoor air quality, financial costs, and CO2 emissions for HVAC operation strategies to mitigate indoor virus in U.S. office buildings. New evaluation metrics are...

How could we predict large-scale building power demand fast and accurately? Generative Adversarial Networks (GAN), as a potential candidate, have recently attracted a lot of attention. This paper identified five promising GANs (Original GAN, cGAN, SGAN, InfoGAN, and ACGAN) and evaluates their performance for predicting building power demand at a...

Do you know that 97% of district heating systems in the US are steam based systems? However, we do not have Modelica models which can simulate such system effectively for practice. As a part of large collaborative research in developing URBANopt and Modelica Buildings library, we created a fast and...

This paper proposes a carbon emission responsive building control framework for controlling thermostatically controllable loads such as heat pumps and heat pump water heaters. The control decisions are made in response to the real-time grid carbon emission intensity signals to shift the loads from “unclean” hours to “clean” hours. The...

While equation-based object-oriented modeling language Modelica can evaluate practical energy improvements for district cooling systems, few have adopted Modelica for this type of large-scale thermo-fluid system. In collaboration with LBNL and NREL through the URBANopt-Modelica project , this work is one of the first district cooling case studies to include...

Large scale power demand prediction for buildings plays a great role in stable operation and management for the grid. To predict large scale power demand in an accurate and fast way, our team has developed a new method called E-GAN, which combines a physics-based model (EnergyPlus) and a data-driven model...

Buildings account for approximately 36% of the United States’ total carbon emissions and building retrofits have great potential to reduce carbon emissions. As a part of a comprehensive research project funded by US National Science Foundation, the Science Foundation Ireland, and the Department for the Economy in Northern Ireland, our...

The COVID-19 pandemic has highlighted a need for strategies to mitigate the risk of airborne disease transmission in shared indoor spaces. In an effort to better understand indoor airborne contaminant transport funded through DTRA and in partnership with LBNL, this work presents a methodology for simultaneously simulating aerosol dispersion from...

It is crucial for buildings to be resilient to unforeseen challenges, such as providing safe indoor environment during the COVID-19 pandemic, while ensuring sustainability for long-term operation. This paper investigates the effectiveness of various strategies to improve indoor air quality during the COVID-19 pandemic for a prototype office building located...