Model-Free Control Design for Loop Heat Pipes Using Deep Deterministic Policy Gradient

Gellrich, Thomas; Yi, Min; Schwab, Stefan; Hohmann, Soeren

doi:10.1016/j.ifacol.2020.12.2190

Cited by 2 publications

(1 citation statement)

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“…Solutions for optimizing the operation of HVAC equipment are considered out of scope if there is no application to manage the indoor environment [60,61], or if the problem formulation is constructed so that the indoor environment is not affected in any way [62][63][64]. Approaches that penalized the RL agent for failing to meet heating or cooling demand were excluded if the penalty was not expressed in terms of indoor environmental variables [65].…”

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A Review of Reinforcement Learning Applications to Control of Heating, Ventilation and Air Conditioning Systems

2022

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Reinforcement learning has emerged as a potentially disruptive technology for control and optimization of HVAC systems. A reinforcement learning agent takes actions, which can be direct HVAC actuator commands or setpoints for control loops in building automation systems. The actions are taken to optimize one or more targets, such as indoor air quality, energy consumption and energy cost. The agent receives feedback from the HVAC systems to quantify how well these targets have been achieved. The feedback is captured by a reward function designed by the developer of the reinforcement learning agent. A few reviews have focused on the reward aspect of reinforcement learning applications for HVAC. However, there is a lack of reviews that assess how the actions of the reinforcement learning agent have been formulated, and how this impacts the possibilities to achieve various optimization targets in single zone or multi-zone buildings. The aim of this review is to identify the action formulations in the literature and to assess how the choice of formulation impacts the level of abstraction at which the HVAC systems are considered. Our methodology involves a search string in the Web of Science database and a list of selection criteria applied to each article in the search results. For each selected article, a three-tier categorization of the selected articles has been performed. Firstly, the applicability of the approach to buildings with one or more zones is considered. Secondly, the articles are categorized by the type of action taken by the agent, such as a binary, discrete or continuous action. Thirdly, the articles are categorized by the aspects of the indoor environment being controlled, namely temperature, humidity or air quality. The main result of the review is this three-tier categorization that reveals the community’s emphasis on specific HVAC applications, as well as the readiness to interface the reinforcement learning solutions to HVAC systems. The article concludes with a discussion of trends in the field as well as challenges that require further research.

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confidence: 99%