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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 developing better batteries for electric vehicles; improving the country's aging electrical grid and innovating distributed energy and storage solutions; developing grid-interactive, efficient buildings; and providing the most comprehensive market and data analysis worldwide.

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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 resilience are key to the foundation of our research, which is driven by technoeconomic analysis and in-lab experimentation and discovery.

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Publications

Publications by Research Area

Buildings
Demand Response
Energy Markets & Policy
Energy Efficiency
Energy Storage
Industrial Energy Analysis
Renewable Energy
Sustainable Energy & Environmental Systems
Transportation

Publications by Division

Building Technology (BTUS)
Energy Analysis (EAEI)
Energy Storage (ESDR)
X Author: Ojas Pradhan

2023

Chen, Zhelun, Zheng O'Neill, Jin Wen, Ojas Pradhan, Tao Yang, Xing Lu, Guanjing Lin, Shohei Miyata, Seungjae Lee, Chou Shen, Roberto Chiosa, Marco Savino Piscitelli, Alfonso Capozzoli, Franz Hengel, Alexander Kuhrer, Marco Pritoni, Wei Liu, John Claub, Yimin Chen, and Terry Herr."A review of data-driven fault detection and diagnostics for building HVAC systems."Applied Energy Vol.339 No..121030 (2023). DOI

2022

Chen, Yimin, Jin Wen, Ojas Pradhan, James Lo, and Teresa Wu."Using discrete Bayesian networks for diagnosing and isolating cross-level faults in HVAC systems."Applied Energy 327 (2022). DOI

2021

Pradhan, Ojas, Jin Wen, Yimin Chen, Xing Lu, Mengyuan Chu, Yangyang Fu, Zheng O'Neill, Teresa Wu, and K. Selcuk Candan."Dynamic Bayesian Network-Based Fault Diagnosis for ASHRAE Guideline 36: High Performance Sequence of Operation for HVAC Systems."The 1st ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities (ACM BALANCES) (2021). DOI
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