A multi-phase genetic algorithm for the efficient management of multi-chiller systems

Beghi, Alessandro; Cecchinato, Luca; Rampazzo, Mirco

doi:10.1016/j.enconman.2010.10.028

Cited by 63 publications

(12 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Since the PFLA is only a secondary data source for the cooling load, it needs to be correlated to the cooling load in order to set up the switch-on/off thresholds. Considering the complex relationship between the electrical power and the associated cooling load, it is not easy to derive an accurate correlation, which leads to uncertainty in the threshold setup [7].…”

Section: Uncertainties Associated With Chiller Sequencing Controlmentioning

confidence: 99%

“…Typical control strategies include chilled water return temperature-based (T-based) sequencing control, bypass flow-based (F-based) sequencing control, direct power-based (P-based) sequencing control, and total cooling load-based (Q-based) sequencing control. These control strategies are widely applied in practice and their performance has significant impacts on system energy efficiency, system stability and indoor thermal comfort [7].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Robustness analysis of chiller sequencing control

Liao

Sun

Huang

2015

Energy Conversion and Management

View full text Add to dashboard Cite

Section: Uncertainties Associated With Chiller Sequencing Controlmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Robustness analysis of chiller sequencing control

Liao

Sun

Huang

2015

Energy Conversion and Management

View full text Add to dashboard Cite

“…Various sequencing controls for multiple cooling units have been developed, including chilled water return-temperature-based control, bypass-flow-based control, and total-cooling-load based control (Liao, Sun, and Huang 2015). These control strategies have been widely implemented in practice and their performances affect the energy efficiency of the cooling plant significantly (Beghi, Cecchinato, and Rampazzo 2011;Liao, Sun, and Huang 2015). *Corresponding author.…”

Section: Introductionmentioning

confidence: 99%

Optimal control strategies of eight parallel heat pumps using Gaussian process emulator

Ahn

Park

Hwang

et al. 2019

Journal of Building Performance Simulation

View full text Add to dashboard Cite

This study describes the development of the optimal control strategies of eight parallel heat pumps in an existing building. The building consists of seven floors above ground and two floors underground with a total floor area of 22,440 m 2. The chilled water generated by each of the eight parallel heat pumps runs through a common primary pipe to multiple airhandling units in the building. Because only one flowmeter and two thermometers (entering and exiting) are installed in the primary pipe, the heat removal rate and efficiency of each heat pump are unknown. The existing control of the heat pumps is as follows: if the chilled water return temperature in the primary pipe becomes greater than a predetermined temperature, the controller increases the number of operating heat pumps. The heat removal rate and efficiency of each heat pump were first identified using a Gaussian process (GP) machine-learning algorithm to develop the optimal control strategy of the eight heat pumps. Two GP models, one for estimating the heat removal rate and the other for estimating the coefficient of performance (COP), were developed based on the measured data for 27 days in July at the sampling time of 15 min. After developing the GP models, the authors applied a COP-based sequencing control strategy to the eight parallel heat pumps. The new optimal control strategy is to switch on the heat pumps in order from highest to lowest COP. Compared with the existing control logic, the new optimal control can reduce energy consumption by 20.9%.

show abstract

“…Chang et al [15] proposed using the cubic polynomial equation to express the performance curve of the chiller and using the gradient method to engage in chiller load distribution optimization in order to obtain the least total power consumption. Beghi et al [16] used the multiobjective genetic algorithm to propose ways to manage energy saving of multiple sets of chiller systems.…”

Section: Introductionmentioning

confidence: 99%

Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

Chen

Chang

2016

Mathematical Problems in Engineering

View full text Add to dashboard Cite

This study covers records of various parameters affecting the power consumption of air-conditioning systems. Using the Support Vector Machine (SVM), the chiller power consumption model, secondary chilled water pump power consumption model, air handling unit fan power consumption model, and air handling unit load model were established. In addition, it was found that 2 of the models all reached 0.998, and the training time was far shorter than that of the neural network. Through genetic programming, a combination of operating parameters with the least power consumption of air conditioning operation was searched. Moreover, the air handling unit load in line with the air conditioning cooling load was predicted. The experimental results show that for the combination of operating parameters with the least power consumption in line with the cooling load obtained through genetic algorithm search, the power consumption of the air conditioning systems under said combination of operating parameters was reduced by 22% compared to the fixed operating parameters, thus indicating significant energy efficiency.

show abstract

A multi-phase genetic algorithm for the efficient management of multi-chiller systems

Cited by 63 publications

References 6 publications

Robustness analysis of chiller sequencing control

Robustness analysis of chiller sequencing control

Optimal control strategies of eight parallel heat pumps using Gaussian process emulator

Support Vector Regression and Genetic Algorithm for HVAC Optimal Operation

Contact Info

Product

Resources

About