Energies | |
Learning Agent for a Heat-Pump Thermostat with a Set-Back Strategy Using Model-Free Reinforcement Learning | |
Frederik Ruelens2  Sandro Iacovella2  Bert J. Claessens1  Ronnie Belmans2  | |
[1] EnergyVille, Thor park 8300, Genk 3600, Belgium; E-Mail:;Division ELECTA, Department of Electrical Engineering, Faculty of Engineering, KU Leuven, Kasteelpark Arenberg 10, Box 2445, Leuven 3001, Belgium; E-Mails: | |
关键词: auto-encoder; batch reinforcement learning; heat pump; set-back thermostat; | |
DOI : 10.3390/en8088300 | |
来源: mdpi | |
【 摘 要 】
The conventional control paradigm for a heat pump with a less efficient auxiliary heating element is to keep its temperature set point constant during the day. This constant temperature set point ensures that the heat pump operates in its more efficient heat-pump mode and minimizes the risk of activating the less efficient auxiliary heating element. As an alternative to a constant set-point strategy, this paper proposes a learning agent for a thermostat with a set-back strategy. This set-back strategy relaxes the set-point temperature during convenient moments, e.g., when the occupants are not at home. Finding an optimal set-back strategy requires solving a sequential decision-making process under uncertainty, which presents two challenges. The first challenge is that for most residential buildings, a description of the thermal characteristics of the building is unavailable and challenging to obtain. The second challenge is that the relevant information on the state,
【 授权许可】
CC BY
© 2015 by the authors; licensee MDPI, Basel, Switzerland.
【 预 览 】
Files | Size | Format | View |
---|---|---|---|
RO202003190008564ZK.pdf | 313KB | download |