Reinforcement learningEquivalent temperatureComfort modelEnergy consumptionVehicle climate control systems aim to keep passengers thermally comfortable. However, current systems control temperature rather than thermal comfort and tend to be energy hungry, which is of particular concern when considering electric ...
To address this challenge, we propose a deep reinforcement learning based framework for energy optimization and thermal comfort control in smart buildings. We formulate the building thermal control as a cost-minimization problem which jointly considers the energy consumption of HVAC and the thermal ...
A Deep reinforcement learning (DRL) algorithm is proposed to control HVAC systems.A hybrid model is developed to simulate dynamics of the building and HVAC system.DRL can co-optimize energy, thermal comfort, CO2, and air quality simultaneously.How decisions are made by DRL under each circumstance...
Thermal soaring, a technique used by birds and gliders to utilize updrafts of hot air, is an appealing model-problem for studying motion control and how it is learned by animals and engineered autonomous systems. Thermal soaring has rich dynamics and non
In particular, the use of RT31 as the phase change material with an inlet temperature of 15 °C was able to control the average temperature of the module at 34.75 °C, and the temperature difference only increased to 2.25 °C. Conversely, by using an inlet temperature of 30 °C was ...
To address this issue, this paper establishes an air conditioning model based on a fuel cell vehicle (FCV) and proposes a control strategy based on thermal comfort, using deep reinforcement learning based on PPO algorithm to establish the control model. Which can automatically control the ...
To address this challenge, we propose a deep reinforcement learning based framework, DeepComfort, for thermal comfort control in buildings. We validate the ... J Li 被引量: 0发表: 2021年 加载更多来源期刊 Building and Environment 2021/06/01 研究点推荐 indoor thermal comfort IoT-based deep le...
Currently, great efforts have been made to develop optimal control models using powerful mathematical computational tools and supervised learning theories, all based on the existence of data sets or big data sets from which it is possible to extract significant information that feeds such emerging mode...
potential of machine learning algorithms in managing this trade-off, such as generation and demand forecast using artificial neutral networks [161] and support vector machines [162], optimization using k-nearest neighbors [163], and real-time optimal control using reinforcement learning [164], [165...
of the present disclosure, based on the thermal image data from the thermal image capture device232, the processor306adjusts the direction of the plasma with the light control signal by adjusting the illuminating timing of the main pulse light and/or the illuminating angle A, A′ of the main...