Name
Abstract | ||
|---|---|---|
ABSTRACTAutomatic 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 results show that the off-policy stochastic latent actor-critic algorithm can maintain the temperature in the predefined range within three months of training without prior knowledge while reducing energy consumption compared to Markovian policies by more than 5%. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1145/3486611.3488730 | Embedded Network Sensor Systems |
DocType | Citations | PageRank |
Conference | 0 | 0.34 |
References | Authors | |
0 | 4 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Marco Biemann | 1 | 0 | 0.34 |
| Xiufeng Liu | 2 | 108 | 14.69 |
| Yifeng Zeng | 3 | 0 | 2.03 |
| Lizhen Huang | 4 | 0 | 0.34 |