Deep Reinforcement Learning-based Building Energy Management using ...
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We propose a Deep Q-Network (DQN)-based building energy management system that reduces the amount of electricity supplied by electric power companies by ...
Abstract—In recent years, stability issues of power grids have become critical with the rapid increase in power consumption. Demand response (DR) is a ...
A Novel Model based Energy Management Strategy for Plug-in Hybrid Electric Vehicles using Deep Reinforcement Learning. Conference Paper. Sep 2023.
Dec 15, 2022 · A promising approach is the participation of charging stations in demand response as load aggregators by coordinating the charging power of ...
"Practical application of energy management strategy for hybrid electric vehicles based on intelligent and connected technologies: Development stages ...
An example outlined in [30] is a vehicle energy management method based on deep reinforcement learning, which pre-processes the vehicle information, the slope, ...
Dec 7, 2022 · In Section 4, the background of deep reinforcement learning is proposed, and the MDP process of the EV charging/discharging behavior is modeled.
Incentive-based demand response for smart grid with reinforcement learning and deep neural network · Model-Free Real-Time EV Charging Scheduling Based on Deep ...
proposed a residential demand response method to minimize energy cost with the consideration of temperature range based on batch reinforcement learning.
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A promising approach is the participation of charging stations in demand response as load aggregators by coordinating the charging power of electric vehicles.