Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network

Li, Ning; Liang, Xin; Deng, Shiming; Xu, Xin; Chan, Ming Yin Jonathan

doi:10.1016/j.apenergy.2011.09.037

Cited by 74 publications

(19 citation statements)

References 32 publications

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“…Developers are working really hard to minimalize the energy consumption of their developed devices; however, there are many imperfections in the actual available product catalogues, technical data, and technical support service systems, especially for the annual energy designing provided by the ventilation producers for building service and energy design engineers [10,11]. Therefore, it would be particularly significant to have measured and recorded data obtained from field studies [12,13], which may be utilized in the course of design work, and which would allow a proper estimation of the expected realizable annual energy consumption of air handling elements in the function of the temperature and relative humidity of ambient and indoor air and operating parameters [14].…”

Section: Introductionmentioning

confidence: 99%

Energy Performance Investigation of a Direct Expansion Ventilation Cooling System with a Heat Wheel

Kassai

2019

Energies

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Climate change is continuously bringing hotter summers and because of this fact, the use of air-conditioning systems is also extending in European countries. To reduce the energy demand and consumption of these systems, it is particularly significant to identify further technical solutions for direct cooling. In this research work, a field study is carried out on the cooling energy performance of an existing, operating ventilation system placed on the flat roof of a shopping center, located in the city of Eger in Hungary. The running system supplies cooled air to the back office and storage area of a shop and includes an air-to-air rotary heat wheel, a mixing box element, and a direct expansion cooling coil connected to a variable refrigerant volume outdoor unit. The objective of the study was to investigate the thermal behavior of each component separately, in order to make clear scientific conclusions from the point of view of energy consumption. Moreover, the carbon dioxide cross-contamination in the heat wheel was also analyzed, which is the major drawback of this type heat recovery unit. To achieve this, an electricity energy meter was installed in the outdoor unit and temperature, humidity, air velocity, and carbon dioxide sensors were placed in the inlet and outlet section of each element that has an effect on the cooling process. To provide continuous data recording and remote monitoring of air handling parameters and energy consumption of the system, a network monitor interface was developed by building management system-based software. The energy impact of the heat wheel resulted in a 624 kWh energy saving and 25.1% energy saving rate for the electric energy consumption of the outdoor unit during the whole cooling period, compared to the system without heat wheel operation. The scale of CO2 cross-contamination in the heat wheel was evaluated as an average value of 16.4%, considering the whole cooling season.

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Section: Introductionmentioning

confidence: 99%

Energy Performance Investigation of a Direct Expansion Ventilation Cooling System with a Heat Wheel

Kassai

2019

Energies

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“…Other authors applied ANNs as a complement to investigate the performance of single-stage refrigeration system using different refrigerants which do not damage the ozone layer [18]. Ning Li et al [19] developed an ANN-based dynamic model for the experimental variable speed direct expansion air conditioning system. Haslinda et al [20] presented a model using steady state input data of an automotive air conditioning system, the model based on an ANN was used to predict: the cooling capacity, the input power and the coefficient of performance.…”

Section: Introductionmentioning

confidence: 99%

Application of artificial neural networks for generation of energetic maps of a variable speed compression system working with R1234yf

Ledesma

Belman-Flores

2014

Applied Thermal Engineering

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“…Numerous studies have attempted to explain the success of ANN with nonlinearity data. This network has also been applied in several areas, such as developing speech recognition, [9][10][11] dynamic modeling, [12][13][14] forecasting in¯nancial¯eld, [15][16][17] and monitoring water quality, 18 just to name a few.…”

Section: Introductionmentioning

confidence: 99%

An Effective Model for Carbon Dioxide Emissions Prediction: Comparison of Artificial Neural Networks Learning Algorithms

Abdullah

Pauzi

2014

Int. J. Comp. Intel. Appl.

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This paper intends to compare various learning algorithms available for training the multi-layer perceptron (MLP) type of arti¯cial neural networks (ANNs). By using di®erent learning algorithms, this study investigates the performances of gradient descent (GD) algorithm; Levenberg-Marquardt (LM) algorithm; and also Boyden, Fletcher, Goldfarb and Shannon (BFGS) algorithm to predict the emissions of carbon dioxide (CO 2 ) in Malaysia. The impact factors of emissions, such as energy use; gross domestic product per capita; population density; combustible renewable and waste; also CO 2 intensity were employed in developing all ANN models investigated in this study. A wide variety of standard statistical performance evaluation measures were employed to evaluate the performances of various ANN models developed. The results obtained in this study indicate that the LM algorithm outperformed both BFGS and GD algorithms.

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Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network

Cited by 74 publications

References 32 publications

Energy Performance Investigation of a Direct Expansion Ventilation Cooling System with a Heat Wheel

Energy Performance Investigation of a Direct Expansion Ventilation Cooling System with a Heat Wheel

Application of artificial neural networks for generation of energetic maps of a variable speed compression system working with R1234yf

An Effective Model for Carbon Dioxide Emissions Prediction: Comparison of Artificial Neural Networks Learning Algorithms

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