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    Energy Technologies Area (ETA) researchers are continually building on the strong scientific foundation we have developed over the past 50 years. We address the world’s most pressing scientific challenges across the buildings, transportation, and industrial sectors. ETA is at the forefront of improving the country's aging electrical grid and innovating distributed energy and storage solutions; developing grid-integrated building systems; and providing the most comprehensive market and data analysis worldwide.

    • Securing U.S. Critical Minerals & Materials Supply Chain
    • Driving Energy Innovation in Data Centers
    • Energy Storage
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    The Energy Technologies Area (ETA) is unique in translating fundamental scientific discoveries into scalable technology adoption. Our approach combines an understanding of the marketplace and the role of state and federal regulation and policies. ETA's research drives real-world, practical results that affect and improve the everyday lives of Americans and those across the globe. Saving energy and increasing reliability are key to the foundation of our research, which is driven by techno-economic analysis and in-lab experimentation and discovery.

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      • Building & Industrial Energy Systems
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Publications

Publications by Research Area

Buildings
Demand Response
Energy Markets & Planning
Energy Efficiency
Energy Storage
Industrial Energy Analysis
Systems and Energy Technologies Analysis
Transportation

Publications by Division

Building & Industrial Energy Systems (BIES)
Energy Analysis (EA)
Energy Technologies & Systems (ETS)
X Author: Kaveh Farokhi Sadabadi

2024

Jin, Ling, Xiaodan Xu, Yuhan Wang, Alina Lazar, Kaveh Farokhi Sadabadi, C Anna Spurlock, Zachary Needell, Duleep Rathgamage Don, Mahyar Amirgholy, and Mona Asudegi."Macroscopic Traffic Modeling Using Probe Vehicle Data: A Machine Learning Approach."Data Science for Transportation 6.3 (2024). DOI
Adlouni, Mohammed, Ling Jin, Xiaodan Xu, C Anna Spurlock, Alina Lazar, Kaveh Farokhi Sadabadi, Mahyar Amirgholy, and Mona Asudegi."Macroscopic Emission Modeling of Urban Traffic Using Probe Vehicle Data: A Machine Learning Approach."2024 IEEE International Conference on Big Data (BigData) (2024) 8601-8603. DOI

2023

Jin, Ling, Xiaodan Xu, Yuhan Wang, Kaveh Farokhi Sadabadi, Alina Lazar, Duleep Rathgamage Don, Zachary Needell, C Anna Spurlock, Mahyar Amirgholy, and Mona Asudegi."Leveraging Probe Data and Machine Learning to Derive and Interpret Macroscopic Fundamental Diagrams Across U.S. Cities."2023 IEEE International Conference on Big Data (BigData) (2023). DOI
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