39 research outputs found
Learning Agent for a Heat-Pump Thermostat With a Set-Back Strategy Using Model-Free Reinforcement Learning
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. A
first challenge is that for most residential buildings a description of the
thermal characteristics of the building is unavailable and challenging to
obtain. A second challenge is that the relevant information on the state, i.e.
the building envelope, cannot be measured by the learning agent. In order to
overcome these two challenges, our paper proposes an auto-encoder coupled with
a batch reinforcement learning technique. The proposed approach is validated
for two building types with different thermal characteristics for heating in
the winter and cooling in the summer. The simulation results indicate that the
proposed learning agent can reduce the energy consumption by 4-9% during 100
winter days and by 9-11% during 80 summer days compared to the conventional
constant set-point strategyComment: Submitted to Energies - MDPI.co
An Efficient Distributed Multi-Agent Reinforcement Learning for EV Charging Network Control
The increasing trend in adopting electric vehicles (EVs) will significantly
impact the residential electricity demand, which results in an increased risk
of transformer overload in the distribution grid. To mitigate such risks, there
are urgent needs to develop effective EV charging controllers. Currently, the
majority of the EV charge controllers are based on a centralized approach for
managing individual EVs or a group of EVs. In this paper, we introduce a
decentralized Multi-agent Reinforcement Learning (MARL) charging framework that
prioritizes the preservation of privacy for EV owners. We employ the
Centralized Training Decentralized Execution-Deep Deterministic Policy Gradient
(CTDE-DDPG) scheme, which provides valuable information to users during
training while maintaining privacy during execution. Our results demonstrate
that the CTDE framework improves the performance of the charging network by
reducing the network costs. Moreover, we show that the Peak-to-Average Ratio
(PAR) of the total demand is reduced, which, in turn, reduces the risk of
transformer overload during the peak hours.Comment: 8 pages, 4 figures, accepted at Allerton 202