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Ongoing Projects
Enhance Data Infrastructure for Building Decarbonization with Large Language Models
The project aims to address the inefficiencies in leveraging big data for building decarbonization studies by utilizing Large Language Models (LLMs) for data management and interpretation. These LLMs are designed to streamline the handling of diverse datasets, organize data in contextually sensitive ways, and simplify access to complex information for users. The project also involves creating a comprehensive data corpus related to building decarbonization and developing a scalable method called 'semantic modeling' for advanced greenhouse gas emission analysis. The initiative has far-reaching societal and educational implications, as it aids in fast-tracking decarbonization in the building sector by offering stakeholders easy access to critical data.
Intelligent Campus Program
NREL's Intelligent Campus program enables researchers and energy managers to study the integration of renewable energy and energy efficiency technologies, make operational decisions that minimize emissions or enhance resiliency, and support a variety of research projects.
Task Description: Developed a probabilistic sequence-to-sequence deep learning algorithm to predict building energy load and PV power generation for (1) advanced building and community operation control and (2) fault detection and diagnostics.
Publication: Probabilistic Variable-Time-Horizon Building Load Forecasting using Deep Learning (Under Review)
Designed validation-grade experiments in iUnit, the standardized experiment chamber at NREL, to quantify the systematic bias and uncertainty of building energy modeling tools including EnergyPlus and TRNSYS
Spearheaded a framework for sequential calibration of timeseries building energy modeling
Publication: Airtightness Measurements and Empirical Data Correlation for Building Energy Simulation (Under Review)
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