A review of optimization approaches for controlling water-cooled central cooling systems

Jia, Lizhi; Wei, Shen; Liu, Junjie

doi:10.1016/j.buildenv.2021.108100

Cited by 33 publications

(4 citation statements)

References 113 publications

(168 reference statements)

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“…At present, a prevailing control strategy is that the set values of control variables of cooling system in terminals are all fixed or partially fixed values, e.g., constant supply and return temperature of chilled water and cooling water in variable flow rate system, constant flow rate of chilled water and cooling water in constant flow rate system, constant flow rate of cooling tower, constant supply air temperature in variable air volume (VAV) system [ 16 , 17 ]. The constant settings of system control make the DCS equipments deviate from the optimal operation status in dynamic conditions, and thus increase power consumption.…”

Section: Operation Control Strategies For Hub Airport Terminalsmentioning

confidence: 99%

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Yan

Yang²,

He³

et al. 2022

Energy

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Section: Operation Control Strategies For Hub Airport Terminalsmentioning

confidence: 99%

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Yan

Yang²,

He³

et al. 2022

Energy

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“…In this study, all applications were divided into five classes as in Figure 12 below. (i) Heating, Ventilation & Air Conditioning, Fans and Water Heaters: Heating, ventilation, and air conditioning (HVAC) systems alone are responsible for about half of the total electricity consumption [39], [90], [91], [92], [93]. In this survey, HVAC, fans and water heaters (WH) have been placed under a single category.…”

Section: Application Classesmentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The twin capabilities of learning from experience and learning at higher levels of abstraction, set reinforcement learning apart from other areas of machine learning and (within the broader context) all of artificial intelligence. It allows algorithmic agents to replace human beings in the real world, including in homes and buildings, in application domains that had hitherto been considered to be beyond today’s capabilities. This goal, specifically aimed at home energy automation that forms the backdrop of this article, which surveys the use of deep reinforcement learning in various HEMS applications. The article provides an overview of generic reinforcement learning. This is followed with discussions on the state-of-the-art methods for value based, policy gradient, and actor-critic methods in deep reinforcement learning. In order to make published literature in reinforcement learning more accessible to HEMS researchers, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using the same terminology as the machine learning community. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. The survey suggests that this research is still in its infancy.

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“…In this study, all applications were divided into five classes as in Figure 12 below. (i) Heating, Ventilation and Air Conditioning, Fans and Water Heaters: Heating, ventilation, and air conditioning (HVAC) systems alone are responsible for about half of the total electricity consumption [48,[101][102][103][104]. In this survey, HVAC, fans and water heaters (WH) have been placed under a single category.…”

Section: Application Classesmentioning

confidence: 99%

Reinforcement Learning: Theory and Applications in HEMS

Alani¹,

Das²

2022

Preprint

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The steep rise in reinforcement learning (RL) in various applications in energy as well as the penetration of home automation in recent years are the motivation for this article. It surveys the use of RL in various home energy management system (HEMS) applications. There is a focus on deep neural network (DNN) models in RL. The article provides an overview of reinforcement learning. This is followed with discussions on state-of-the-art methods for value, policy, and actor–critic methods in deep reinforcement learning (DRL). In order to make the published literature in reinforcement learning more accessible to the HEMS community, verbal descriptions are accompanied with explanatory figures as well as mathematical expressions using standard machine learning terminology. Next, a detailed survey of how reinforcement learning is used in different HEMS domains is described. The survey also considers what kind of reinforcement learning algorithms are used in each HEMS application. It suggests that research in this direction is still in its infancy. Lastly, the article proposes four performance metrics to evaluate RL methods.

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A review of optimization approaches for controlling water-cooled central cooling systems

Cited by 33 publications

References 113 publications

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Strategical district cooling system operation in hub airport terminals, a research focusing on COVID-19 pandemic impact

Reinforcement Learning: Theory and Applications in HEMS

Reinforcement Learning: Theory and Applications in HEMS

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