Towards addressing this research gap, this paper presents a model-free reinforcement learning-based controller for a day-ahead price market. The controller aims ...
The controller aims to minimize the electricity cost of water heater while maintaining the comfort of the water heater users. There has been significant ...
Electric water heaters represent 14% of the electricity consumption in the residential buildings and the cost associated with domestic water heating account ...
Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater ... optimise indoor temperature control and heating energy consumption in ...
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February 2021. Double Deep Q-Networks for Optimizing Electricity Cost of a Water Heater... Conference Paper. Pagination. First page « First · Previous page ...
Mar 15, 2024 · This paper proposes a new strategy for variable water temperature setpoints. The objective is to minimize energy costs while maximizing occupant comfort.
Missing: Double Q-
Feb 3, 2023 · This paper presents a reinforcement learning (RL)-based method to control the heating for minimizing the heating electricity cost and shifting the electricity ...
As known from previous research [1,16, 17] , RL (without pre-training) manages to reduce cost compared to a hysteresis controller. Here, cost has been reduced ...
In (Amasyali et al., 2021), au- thors train different agents using DQN to minimize electricity cost of water heater without causing dis- comfort to users. Their ...
In this paper, a home energy management optimization strategy is proposed based on deep Q- learning (DQN) and double deep Q-learning (DDQN) to perform.