Jonathan Lee
University of California, Berkeley
21 Papers
19 Citations
Jonathan Lee is an academic researcher from University of California, Berkeley. The author has contributed to research in topics: Computer science & Renewable energy. The author has an hindex of 5, co-authored 16 publications.
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Papers
Automatic Fault Location on Distribution Networks Using Synchronized Voltage Phasor Measurement Units
Jonathan Lee
- 28 Jul 2014
TL;DR: In this article, the authors describe how PMU data during a fault event can be used to accurately locate faults on the primary distribution system, rather than requiring many specialized line sensors to enable fault location.
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Computational experiment design for operations model simulation
Jose Daniel Lara,Jose Daniel Lara,Jonathan Lee,Duncan S. Callaway,Bri-Mathias Hodge,Bri-Mathias Hodge +5 more
TL;DR: In this paper, scientific computing best-practices for the validation and reproduction of power systems operational models are developed and two case studies are employed to demonstrate the proposed validate and reproduction framework.
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Non-Intrusive Load Management Under Forecast Uncertainty in Energy Constrained Microgrids
TL;DR: It is shown that using forecasts to schedule limits can significantly improve power availability and the customers' benefits from consumption, even without the controller having a full model of the customers’ responses.
10
Malasakit 1.0: A participatory online platform for crowdsourcing disaster risk reduction strategies in the philippines
Brandie Nonnecke,Shrestha Mohanty,Andrew H. Lee,Jonathan Lee,Sequoia Beckman,Justin Mi,Sanjay Krishnan,Rachel Edita Roxas,Nathaniel Oco,Camille Crittenden,Ken Goldberg +10 more
- 01 Oct 2017
TL;DR: Results suggest that female participants are more confident than males in their community's ability to recover from a major typhoon, and Malasakit uses dimensionality reduction and peer-to-peer evaluation on qualitative textual suggestions to identify locally appropriate DRR strategies.
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Sequential robot imitation learning from observations
TL;DR: In this paper, a framework to learn the sequential structure in the demonstrations for robot imitation learning is presented. But this framework is not suitable for the task of human imitation learning, as shown in Figure 1.
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