Our paper, which addresses the scenario forecasts problem in renewables generation has been accepted to Power Systems Computation Conference 2018! Take a look at our pre-print paper here: https://arxiv.org/abs/1711.02247
02/12 CEI Travel Award awarded for the conference.
I was invited to 2nd Physics Informed Machine Learning Conference hosted by Los Alamos National Laboratory in Santa Fe. Here is our poster on generative models for renewables.
My lab mate, Yuanyuan Shi, presented our joint work at INFORMS Annual Meeting 2017 for Learning, optimization and control for resilient power grids. The work is focused on building energy control and optimization via deep learning. It’s quite a unique, general optimal control approach which utilizes deep learning tools for system modeling and optimization. Take a look at our slides: Informs_talk.
Thanks to the Clean Energy Institute (CEI), I was awarded a CEI graduate fellowship to support my research on learning and optimization for cyber-physical systems for clean energy!
For more info, please take a look at: http://www.cei.washington.edu/persons/yize-chen/
Paper entitles “Model-Free Renewable Scenario Generation Using Generative Adversarial Networks”
A work with Yishen Wang, Daniel Kirschen and Baosen Zhang on renewables scenarios generation has been submitted to IEEE Transactions on Power Systems Special Section on
Enabling very high penetration renewable energy integration into future power systems. Take a loot at our Arxiv version: https://arxiv.org/pdf/1707.09676.pdf
Update: Jan.17th 2017 Paper accepted to IEEE Transactions on Power Systems. Preprint: http://ieeexplore.ieee.org/document/8260947/
Paper on building energy management and control based on deep learning: Modeling and Optimization of Complex Building Energy Systems with Deep Neural Networks by Yize Chen, Yuanyuan Shi and Baosen Zhang accpeted by Asilomar Conference on Signals, Systems, and Computers 2017.
Take a look at our work! https://arxiv.org/pdf/1703.04318.pdf
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Having come to Seattle for a month. Getting used to the transfer from undergraduate to a Ph.D. student