The adaptation was applied to train multiple agents to control DHW systems in order to find possible trade-offs between comfort and energy cost reduction.
Jul 21, 2024 · This work suggests an adaptation of Deep Q-Networks (DQN) to solve multi-objective sequential decision problems using scalarization functions.
Mar 15, 2023 · This work suggests an adaptation of Deep Q-Networks (DQN) to solve multi-objective sequential decision problems using scalarization functions.
Mar 15, 2024 · This study describes the implementation of a DRL platform that allows training smart agents to manage a complex water heating system.
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Aug 15, 2023 · The present study deals with a multi-objective analysis of an innovative decentralised system to produce and store domestic hot water (DHW).
Oct 22, 2024 · MORL has been widely used in energy management, e.g. micro-grid control [40,20], water heating system oversight [31] , energy control in ...
This study proposes a reinforcement learning (RL) approach that aims to minimize the electricity consumption and the CO2 emissions of a heat pump water ...
Oct 29, 2019 · A deep Q-network (DQN) was applied for model-free optimal control balancing between different HVAC systems.
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Nov 16, 2021 · This study proposes a model-free deep reinforcement learning (RL) approach that aims to minimize the electricity cost of a water heater under a time-of-use ( ...
May 12, 2022 · This research study presents a multi-objective tunable deep reinforcement learning algorithm for demand-side management of household appliances.
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