Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions

Kim, Dongwoo; Song, Kang Sub; Lim, Jin‐Ho; Kim, Yongchan

doi:10.1016/j.energy.2018.05.046

Cited by 17 publications

(7 citation statements)

References 36 publications

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“…The ANN is one of the machine learning models inspired by simulating physical signals and the neuron system of the human brain. The ANN model allows to solve physical phenomena or engineering problems without a clear equation [ [31] , [32] , [33] , [34] , [35] , [36] , [37] ]. Fig.…”

Section: Predictive Analysis Of the Impact Of Covid-19 In Dhw Demandmentioning

confidence: 99%

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Kim

Yim

Lee

2021

Energy

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COVID-19 made considerable changes in the lifestyle of people, which have led to a rise in energy use in homes. So, this study investigated the relationship between COVID-19 and domestic hot water demands. For this purpose, a nondimensional and principal component analysis were conducted to find out the influencing factors using demand data before and after COVID-19 from our study site. Analysis showed that the COVID-19 outbreak affected the daily peak time and the amount of domestic hot water usage, the active case number of COVID-19 was a good indicator for correlating the changes in hot water demand and patterns. Based on this, a machine learning model with an artificial neural network was developed to predict hot water demand depending on the severity of COVID-19 and the relevant correlation was also derived. The model analysis showed that the increase in the number of active cases in the region affected the hot water demand increased at a certain rate and the maximum demand peak in morning during weekdays and weekends decreased. Furthermore, if the number of active cases reached more than 4000, the peak in morning moved to afternoon so that the energy use patterns of weekdays and weekends are assimilated.

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Section: Predictive Analysis Of the Impact Of Covid-19 In Dhw Demandmentioning

confidence: 99%

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Kim

Yim

Lee

2021

Energy

Self Cite

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“…Simulation experiments of different loads were carried out through the established multi-subsystem coupling model (15) and collected dynamic data of inlet temperature, pressure, and exhaust flow. Then the data on temperature, pressure, and flow were preprocessed under a stable state of each load segment.…”

Section: Testing the Performance Of The Ann Modelsmentioning

confidence: 99%

“…Barroso-Maldonado et al developed two models: one using an artificial neural network and another one using a probabilistic neural network to predict and simulate the behavior of a reciprocating compressor [14]. The artificial neural network models for the non-injection, vapor injection, and two-phase injection heat pumps were developed to predict the performance indexes during cooling and heating seasons [15]. An ANN was trained and validated with the experimental data and the same was proposed for the predicting performance of a work recovery scroll expander in closed-loop operation with a CO 2 refrigeration system in the sub-critical zone [16].…”

Section: Introductionmentioning

confidence: 99%

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

et al. 2020

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The regulating performance degradation of the stepless capacity regulation system for reciprocating compressors occurs frequently in long-term operations. It affects the safe and stable operation of the compressor seriously. The degradation mechanisms in a stepless capacity regulation system are mainly caused by valve leakage, degeneration of the reset spring of the unloader, and (or) deviation of the solenoid valve’s characteristic parameters. In this study, to research the system performance degradation mechanisms and the influence of control parameters on system behavior, a multi-subsystem mathematics model which integrates compressor, gas pipeline, buffer tank, and actuator was built. In order to calculate the rate of degradation, a load prediction model based on a modified back-propagation neural network was established. The rate of degradation can be calculated using the predicted results. In order to optimize system regulation performance, a degradation-based optimization framework was developed which determines optimum control parameter compensation to achieve a minimum degradation rate. In addition, in order to avoid over-compensation, an adaptive control parameter compensation optimization method was adopted. According to the deviation between the given load and the prediction load, the control parameter compensations are obtained adaptively. Finally, two optimization experiments are carried out to show the effectiveness of the developed framework. The optimization results illustrate the degradation rate of the system gradually returning to normal during 60s without any over-compensation.

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“…Numerous experimental and numerical studies have been conducted to explore the benefits of the VI technique in the literature [6][7][8][9][10][11][12][13][14][15][16][17][18]. For example, research has shown that the VI technique can significantly improve the thermal performance of a vapor compression heating cycle, particularly in cold regions [19].…”

Section: Introductionmentioning

confidence: 99%

Enhancing Energy Efficiency of Air-Conditioning Systems in Hot Climates through Refrigerant Vapor Injection: A Study of Kuwait and GCC Countries

Bahman

Abdal

2023

International Journal of Energy Research

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This study is aimed at investigating the potential of the refrigerant vapor injection (VI) technique to improve the energy efficiency of air-conditioning (AC) systems in hot climates, with a focus on Kuwait’s sustainability goals for 2035. The VI technique was found to significantly enhance heating and cooling systems by lowering compressor discharge temperature and improving cooling capacity, resulting in an enhanced coefficient of performance (COP). Data from previous experimental studies were analyzed to correlate the VI technique’s cooling COP with operating conditions at high ambient temperatures ranging from 20°C to 52°C. The results showed that the VI technique was applicable at high ambient temperatures, leading to improvements of up to 35.4% compared to conventional cooling systems. Empirical formulas, comprised of the temperature parameters (ambient, wet bulb, condensation, and evaporation temperatures), were developed to predict the COP of the VI cooling cycles with a maximum mean absolute error (MAE) of 3.81%. The results also showed that the VI technique enhanced the cooling COP by up to 28% when operating at 51.7°C and up to 34% during the hottest month in Kuwait (July) compared to the conventional cooling cycle. The empirical correlations were also applied to other Gulf Cooperation Council (GCC) countries to examine the impact of ambient humidity on cooling COP, with predicted increases ranging from 13.5% to 21.2%. Further experimental studies are recommended to fully evaluate the cooling impact of the VI system in GCC countries.

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Analysis of two-phase injection heat pump using artificial neural network considering APF and LCCP under various weather conditions

Cited by 17 publications

References 36 publications

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Analytical study on changes in domestic hot water use caused by COVID-19 pandemic

Performance Degradation Analysis and Optimization of the Stepless Capacity Regulation System for Reciprocating Compressors

Enhancing Energy Efficiency of Air-Conditioning Systems in Hot Climates through Refrigerant Vapor Injection: A Study of Kuwait and GCC Countries

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