Two-layer control of multi-chiller systems

Beghi, Alessandro; Cecchinato, Luca; Cosi, Giovanni; Rampazzo, Mirco

doi:10.1109/cca.2010.5611191

Cited by 6 publications

(2 citation statements)

References 4 publications

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“…The series arrangement is better because it can transfer heat, but it is not with a better efficiency than Parallel. Every chiller can be operated at various capacities to meet various refrigeration demands [26] [27]. One of the most important problems with MCSs is to save more energy by reducing the total electrical energy consumption of the chillers, since the electrical consumption of MCSs increases significantly when the chillers are poorly handled; improving the efficiency of MCSs may result in substantial energy savings.…”

Section: Chillersmentioning

confidence: 99%

Energy Saving by Reinforcement Learning for Multi-Chillers of HVAC Systems

Hussein¹,

Ateeq²,

Homod³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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This paper presents a method for controlling and operating a multi-chillers system: (1) Model-based control approach was used by MATLAB/SIMULINK to model a building containing two non-identical chillers depending on thermal loads. (2) ON/OFF all chillers alternately using the model reinforcement learning controller (RL-control) to select the appropriate chiller for the building conditioning process. The results were in terms of energy efficiency and performance of the enhanced learning control for the chiller, and a control unit signal (PID) was applied to make a comparison with the signals of energy, power, and temperatures. After comparison, it was found that the energy saving through the proposed controller is 45% of the traditional (PID) strategy, where can the proposed strategy control for the chiller appropriate for the building's conditioning process.

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Section: Chillersmentioning

confidence: 99%

Energy Saving by Reinforcement Learning for Multi-Chillers of HVAC Systems

Hussein¹,

Ateeq²,

Homod³

2022

Proceedings of 2nd International Multi-Disciplinary Conference Theme: Integrated Sciences and Technologies, IMDC-IST 2021, 7-9

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“…A two-layer controller has been proposed by Beghi et al for a multiple chiller to solve the problem of optimal chiller loading (OCL) and sequence using PSO [17]. In the first layer, the scheduling stage determines which chillers are loaded during each sub-time period, while the second layer for the operating stage was used to calculate the operation conditions in each sub-time period.…”

Section: Review Of Pso Techniquesmentioning

confidence: 99%

An Application of a Novel Technique for Assessing the Operating Performance of Existing Cooling Systems on a University Campus

et al. 2018

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Abstract:Optimal operation is an important aspect of energy efficiency that can be employed to reduce power consumption. In cooling systems, the chillers consume a large amount of electricity, especially if they are not optimally operated, therefore, they cannot produce the required or rated cooling load capacity. The objective of this paper is to improve coefficient of performance (COP) for the operation of chillers and to reduce power consumption. Two contributions in this work are: (1) the prediction of a model by using Adaptive Neuro-Fuzzy Inference System (ANFIS)-based Fuzzy Clustering Subtractive (FCS), and (2) the classification and optimization of the predicted models by using an Accelerated Particle Swarm Optimization (APSO) algorithm. Particularly, in contribution (1), two models are developed to predict/assess power consumption and cooling load capacity. While in contribution (2), the predictive model's data obtained are used to classify the operating performance of the chiller and to optimize the model in order to reduce power consumption and cooling capacity. Therefore, data classification by APSO is used to enhance the coefficient of performance (COP). The proposed technique reduces the total power consumption by 33.2% and meets the cooling demand requirements. Also, it improves the cooling performance based on COP, thus resulting in a 15.95% increase in efficiency compared to the existing cooling system. The studied ANFIS-based FCS outperforms the ANFIS-based fuzzy C-means clustering in terms of the regression. Then, the algorithm-based classifier APSO has better results compared to the conventional particle swarm optimization (PSO). The data was acquired from the District Cooling System (DCS) at the Universiti Teknologi Petronas (UTP) campus in Malaysia.

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