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Automated Coordination of Distributed Energy Resources using Local Energy Markets and Reinforcement Learning
DownloadFall 2024
The conventional unidirectional model of the electricity grid operations is no longer sufficient. The continued proliferation of distributed energy resources and the resultant surge in net load variability at the grid edge necessitates deploying adequate demand response methods. This thesis...
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Spring 2013
Cost overrun and schedule slippage are common problems for mega industrial construction projects. Lack of effective planning and scheduling tools is identified as a major contributing factor to poor project performance. Planning and scheduling tools should be custom designed to address the...
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2006
Wang, Tao, Schuurmans, Dale, Bowling, Michael
Technical report TR06-26. We investigate the dual approach to dynamic programming and reinforcement learning, based on maintaining an explicit representation of stationary distributions as opposed to value functions. A significant advantage of the dual approach is that it allows one to exploit...
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Extending Differentiable Programming to include Non-differentiable Modules using Differentiable Bypass for Combining Convolutional Neural Networks and Dynamic Programming into an End-to-end Trainable Framework
DownloadSpring 2019
Differentiable Programming is the paradigm where different functions or modules are combined into a unified pipeline with the purpose of applying end-to-end learning or optimization. A natural impediment is the non-differentiability characteristic of many modules. This thesis proposes a new way...
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Fall 2015
We study a channel selection problem in cognitive radio with imperfect spectrum sensing. In this problem, a secondary (unlicensed) user must select a subset of M channels out of N channels to sense. The user then accesses up to K ≤ M channels that were sensed free. The objective is to maximize...