In-situ application of an ANN algorithm for optimized chilled and condenser water temperatures set-point during cooling operation

Kang, Woo‐Hyoung; Yoon, Yeobeom; Lee, Je Hyeon; Song, Kihyung; Chae, Young Tae

doi:10.1016/j.enbuild.2020.110666

Cited by 24 publications

(7 citation statements)

References 23 publications

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“…As previously mentioned, the optimal control of HVAC systems is necessary. Before 2013, model-based control (such as the optimal control methods proposed by Kang et al [10], Huang et al [8], and other researchers) was the mainstream of the building optimal control field [2]. Apart from this, model-free optimal control based on RL has been drawing increasingly more attention in the building system control domain since 2013 [2].…”

Section: Value and Application Of Reinforcement Learning (Rl) Techniq...mentioning

confidence: 99%

“…In buildings, RL techniques have been used to control various subjects from windows and batteries to hot domestic water systems and HVAC systems. In the HVAC system control field, control variables include the indoor temperature setpoint [15], chilled water temperature setpoint [10,16], and the cooling tower fan frequency [11].…”

Section: Value and Application Of Reinforcement Learning (Rl) Techniq...mentioning

confidence: 99%

“…As a sub-system of the HVAC system, a condenser water loop accounts for the heat rejection of the central chiller plant. Moreover, the operation quality of this loop can considerably influence the overall efficiency of the whole HVAC system [5][6][7][8][9][10][11]. The control subject of this study is the condenser water loop.…”

Section: Introductionmentioning

confidence: 99%

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Comparative Evaluation of Different Multi-Agent Reinforcement Learning Mechanisms in Condenser Water System Control

Qiu

Qian

2022

Buildings

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Model-free reinforcement learning (RL) techniques are currently drawing attention in the control of heating, ventilation, and air-conditioning (HVAC) systems due to their minor pre-conditions and fast online optimization. The simultaneous optimal control of multiple HVAC appliances is a high-dimensional optimization problem, which single-agent RL schemes can barely handle. Hence, it is necessary to investigate how to address high-dimensional control problems with multiple agents. To realize this, different multi-agent reinforcement learning (MARL) mechanisms are available. This study intends to compare and evaluate three MARL mechanisms: Division, Multiplication, and Interaction. For comparison, quantitative simulations are conducted based on a virtual environment established using measured data of a real condenser water system. The system operation simulation results indicate that (1) Multiplication is not effective for high-dimensional RL-based control problems in HVAC systems due to its low learning speed and high training cost; (2) the performance of Division is close to that of the Interaction mechanism during the initial stage, while Division’s neglect of agent mutual inference limits its performance upper bound; (3) compared to the other two, Interaction is more suitable for multi-equipment HVAC control problems given its performance in both short-term (10% annual energy conservation compared to baseline) and long-term scenarios (over 11% energy conservation).

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Section: Value and Application Of Reinforcement Learning (Rl) Techniq...mentioning

confidence: 99%

Section: Value and Application Of Reinforcement Learning (Rl) Techniq...mentioning

confidence: 99%

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Comparative Evaluation of Different Multi-Agent Reinforcement Learning Mechanisms in Condenser Water System Control

Qiu

Qian

2022

Buildings

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“…Kang et al. [10] considered the outlet temperature of condensed water in a cooling tower and the outlet temperature of chilled water in chillers as system control variables, and analyzed the refrigeration energy‐saving performance through field application and actual measurement.…”

Section: Introductionmentioning

confidence: 99%

“…[2][3][4][5][6] and [7][8][9], the authors reduced the energy consumption of chillers from the perspective of load forecasting, and from the perspective of chillers configuration and the optimal sequence control strategies, respectively. Kang et al [10] considered the outlet temperature of condensed water in a cooling tower and the outlet temperature of chilled water in chillers as system control variables, and analyzed the refrigeration energy-saving performance through field application and actual measurement.…”

Section: Introductionmentioning

confidence: 99%

Application of improved particle swarm optimization in the energy saving strategy for a central air‐conditioning system

Wang

2023

IET Generation Trans & Dist

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Here, a new energy‐saving optimization method, known as the Improved Particle Swarm Optimization (IPSO) algorithm, is proposed to solve the practical engineering problem of high energy consumption during the operation of central air‐conditioning water systems. The mathematical model of total energy consumption optimization and the calculation principle of the IPSO are established. It is shown via experiments that the proposed algorithm requires a short optimization time and can achieve the optimal operation conditions of water systems under various cooling loads, where these conditions include the optimal control strategy of equipment, and water temperature at the inlet and outlet of chillers. Furthermore, it ensures the lowest energy consumption of water system operation. The measured results show that the comprehensive energy saving rate of water system operation can reach more than 18% compared with that before the system transformation.

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Balancing energy consumption and thermal comfort in buildings: a multi-criteria framework

Wani,

Hafiz,

Swain

et al. 2023

Ann Oper Res

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In-situ application of an ANN algorithm for optimized chilled and condenser water temperatures set-point during cooling operation

Cited by 24 publications

References 23 publications

Comparative Evaluation of Different Multi-Agent Reinforcement Learning Mechanisms in Condenser Water System Control

Comparative Evaluation of Different Multi-Agent Reinforcement Learning Mechanisms in Condenser Water System Control

Application of improved particle swarm optimization in the energy saving strategy for a central air‐conditioning system

Balancing energy consumption and thermal comfort in buildings: a multi-criteria framework

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