Research Themes +
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 climate challenges by bringing to market energy-efficient innovations 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 for renewable technologies like wind and solar.
Strategic Initiatives +
The Energy Technologies Area (ETA) Strategic Plan is the guiding force for our research and development for the next ten years. It clearly charts a path toward clean-energy solutions and focuses on five detailed Strategic Initiatives. The Plan provides an in-depth look at how ETA is accelerating research to provide affordable, clean energy to all while accomplishing deep, economy-wide decarbonization, looking to avoid a rise in global average temperature while simultaneously developing solutions to increase humanity's resilience to extreme weather volatility.
Publications
News +
For media inquiries,
please contact ETA
Interim Communications Manager
Kiran Julin

kjulin@lbl.gov
About Us +
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 battling the Climate Crisis are key to the foundation of our research, which is driven by technoeconomic analysis and in-lab experimentation and discovery.

Publications

Publications by Research Area

Demand Response

Electricity Markets & Policy

Energy Efficiency

Industrial Energy Analysis

Renewable Energy

Sustainable Energy & Environmental Systems

Publications by Division

Building Technology (BTUS)

Energy Analysis (EAEI)

Energy Storage (ESDR)

2023

Granderson, Jessica, Guanjing Lin, Yimin Chen, Armando Casillas, Jin Wen, Zhelun Chen, Piljae Im, Sen Huang, and Jiazhen Ling."A labeled dataset for building HVAC systems operating in faulted and fault-free states."Scientific Data 10 (2023). DOI

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 Pritonic, 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

Chen, Yimin, Guanjing Lin, Zhelun Chen, Jin Wen, and Jessica Granderson."A simulation-based evaluation of fan coil unit fault effects."Energy and Buildings 263 (2022). DOI

Chen, Chien-fei, Thomas Dietz, Nina H Fefferman, Jamie Greig, Kristen Cetin, Caitlin Robinson, Laura Arpan, Marcel Schweiker, Bing Dong, Wenbo Wu, Yue Li, Hongyu Zhou, Jianzhong Wu, Jin Wen, Joshua S Fu, Tianzhen Hong, Da Yan, Hannah Nelson, Yimin Zhu, Xueping Li, Le Xie, and Rachel Fu."Extreme events, energy security and equality through micro- and macro-levels: Concepts, challenges and methods."Energy Research & Social Science 85 (2022) 102401. DOI

Chen, Yimin, Jin Wen, and James Lo."Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part II Field Evaluation."Journal of Engineering for Sustainable Buildings and Cities (2022). DOI

Chen, Yimin, Jin Wen, and James Lo."Using Weather and Schedule based Pattern Matching and Feature based PCA for Whole Building Fault Detection — Part I Development of the Method."ASME Journal of Engineering for Sustainable Buildings and Cities 3 (2022).

Chen, Yimin, Zhelun Chen, Guanjing Lin, Jin Wen, and Jessica Granderson."A simulation-based method to analyze fan coil unit fault impacts."2022 ASHRAE Summer Conference (2022).

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