Journal
IEEE TRANSACTIONS ON SMART GRID
Volume 9, Issue 4, Pages 3259-3269Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TSG.2016.2629450
Keywords
Convolutional neural network; deep learning; demand response; reinforcement learning
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Funding
- Flemish Agency for Innovation through Science IWT through Science and Technology, promoting Strategic Basic Research [IWT-SBO-SMILE-IT/140047]
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Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability. This paper proposes a novel approach that uses a convolutional neural network (CNN) to extract hidden state-time features to mitigate the curse of partial observability. More specific, a CNN is used as a function approximator to estimate the state-action value function or Q-function in the supervised learning step of fitted Q-iteration. The approach is evaluated in a qualitative simulation, comprising a cluster of thermostatically controlled loads that only share their air temperature, while their envelope temperature remains hidden. The simulation results show that the presented approach is able to capture the underlying hidden features and able to successfully reduce the electricity cost the cluster.
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