Proceedings of the 8th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation 2021
DOI: 10.1145/3486611.3488730
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Addressing partial observability in reinforcement learning for energy management

Abstract: Automatic control of energy systems is affected by the uncertainties of multiple factors, including weather, prices and human activities. The literature relies on Markov-based control, taking only into account the current state. This impacts control performance, as previous states give additional context for decision making. We present two ways to learn non-Markovian policies, based on recurrent neural networks and variational inference. We evaluate the methods on a simulated data centre HVAC control task. The… Show more

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Cited by 2 publications
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