Inside the Black Box: Understanding key drivers of global emission scenarios. - Jonathan Koomey; Zachary Schmidt; Holmes Hummel; John Weyant (Environ. Model. Softw., 2019)

Exploring the black box: Applying macro decomposition tools for scenario comparisons. - Jonathan Koomey; Zachary Schmidt; Karl Hausker; Dan Lashof (Environ. Model. Softw., 2022)

Empirical Measurements of AI Training Power Demand on a GPU-Accelerated Node. - Imran Latif; Alex C. Newkirk; Matthew R. Carbone; Arslan Munir; Yuewei Lin; Jonathan Koomey; Xi Yu; Zhiuha Dong (CoRR, 2024)

Single-Node Power Demand During AI Training: Measurements on an 8-GPU NVIDIA H100 System. - Imran Latif; Alex C. Newkirk; Matthew R. Carbone; Arslan Munir; Yuewei Lin; Jonathan Koomey; Xi Yu; Zhihua Dong (IEEE Access, 2025)

Generalizable Machine Learning Models for Predicting Data Center Server Power, Efficiency, and Throughput. - Nuoa Lei; Arman Shehabi; Jun Lu; Zhi Cao; Jonathan Koomey; Sarah Smith; Eric R. Masanet (CoRR, 2025)

Empirically-Calibrated H100 Node Power Models for Reducing Uncertainty in AI Training Energy Estimation. - Alex C. Newkirk; Jared Fernandez; Jonathan Koomey; Imran Latif; Emma Strubell; Arman Shehabi; Constantine Samaras (CoRR, 2025)