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Soteris A. Kalogirou - One of the best experts on this subject based on the ideXlab platform.

  • Simulation of a solar Domestic Water Heating system using a time marching model
    Renewable Energy, 2002
    Co-Authors: Milorad Bojić, Soteris A. Kalogirou, K. Petronijević
    Abstract:

    This paper presents the modelling and simulation of a solar Water Heating system using a time marching model. The results of simulations performed on an annual basis for a solar system, constructed and operated in Yugoslavia, which provides Domestic hot Water for a four-person family are presented. The solar Water heater consists of a flat-plate solar collector, a Water-storage tank, an electric heater, and a Water-mixing device. The mathematical model is used to evaluate the annual variation of the solar fraction with respect to the volume of the storage tank, demand hot Water temperature required, difference of this temperature and preset storage tank Water temperature, and consumption profile of the Domestic hot Water demand. The results of this investigation may be used to design a solar collector system, and to operate already designed systems, effectively. The results for a number of designs with different storage tank volumes indicate that the systems with greater volume yield higher solar fraction values. The results additionally indicate that the solar fraction of the system increases with lower hot Water demand temperature and higher differences between the mean storage Water and the demand temperatures. However, when a larger storage tank volume is used, the solar fraction is less sensitive to a variation of these operation parameters.

  • long term performance prediction of forced circulation solar Domestic Water Heating systems using artificial neural networks
    Applied Energy, 2000
    Co-Authors: Soteris A. Kalogirou
    Abstract:

    The objective of this work is to use Artificial Neural Networks (ANNs) for the long-term performance prediction of forced circulation type solar Domestic Water Heating (SDWH) systems. ANNs have been used in diverse applications and they have been shown to be particularly useful in system modelling and for system identification. Three SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. Two ANNs have been trained using the monthly data produced by the modelling program supplied with the standard. Different networks were used due to the different natures of the output required in each case. The first network was trained to estimate the solar energy output of the system for a draw-off quantity equal to the storage tank capacity and the second network was trained to estimate the solar energy output of the system and the average quantity of hot Water per month, at demand temperatures of 35 and 40°C. The data presented as input to both networks are similar to the data used in the program supplied with the standard. The statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9972 for the first network and equal to 0.9878 and 0.9973 for the second network for the two output parameters, solar energy output and hot Water quantity, respectively. Other data, unknown to the network, were subsequently used to evaluate the accuracy of the prediction. Predictions with R2-values equal to 0.9945 for the first network and 0.9825 and 0.9910 for the second were obtained. The maximum percentage differences were 1.9 and 5.5% for the two networks respectively. These results indicate that the proposed method can successfully be used for the prediction of the long-term performance of forced circulation Water Heating solar systems. The advantages of this approach compared to the conventional algorithmic methods are speed, simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network.

  • thermosiphon solar Domestic Water Heating systems long term performance prediction using artificial neural networks
    Solar Energy, 2000
    Co-Authors: Soteris A. Kalogirou, S D Panteliou
    Abstract:

    Abstract The objective of this work is to use artificial neural networks (ANN) for the long-term performance prediction of thermosiphonic type solar Domestic Water Heating (SDWH) systems. Thirty SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. From these, data from 27 of the systems were used for training and testing the network while data from the remaining three were used for validation. Two ANNs have been trained using the monthly data produced by the modeling program supplied with the standard ISO 9459-2. Different networks were used depending on the nature of the required output, which is different in each case. The first network was trained to estimate the solar energy output of the system for a draw-off quantity equal to the storage tank capacity (at the end of the solar energy collection period) and the second one was trained to estimate the solar energy output of the system and the average quantity of hot Water per month at demand temperatures of 35 and 40°C. The collector areas of the considered systems were varying between 1.81 m2 and 4.38 m2. Open and closed thermosiphonic systems have been considered both with horizontal and vertical storage tanks. In this way the networks were trained to accept and handle a number of unusual cases. The input data in both networks are similar to the ones used in the program supplied with the standard. These were the size and performance characteristics of each system and various climatic data. In the second network the demand temperature was also used as input. For the first network the statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9993. For the second network the R2-value for the two output parameters was equal to 0.9848 and 0.9926, respectively. Unknown data were subsequently used to investigate the accuracy of prediction and R2-values equal to 0.9913 for the first network and 0.9733 and 0.9940 for the second were obtained. These results indicate that the proposed method can successfully be used for the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank or for the solar energy output of the system and the average quantity of the hot Water per month for the two demand Water temperatures considered.

  • modeling of solar Domestic Water Heating systems using artificial neural networks
    Solar Energy, 1999
    Co-Authors: Soteris A. Kalogirou, S D Panteliou, A J Dentsoras
    Abstract:

    Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can be trained to predict results from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly useful in system modeling and for system identification. The objective of this work was to train an ANN to learn to predict the useful energy extracted and the temperature rise in the stored Water of solar Domestic Water Heating (SDHW) systems with the minimum of input data. An ANN has been trained based on 30 known cases of systems, 22 varying from collector areas between 1.81 m and 4.38 m . Open and closed systems have been considered both with horizontal and vertical storage tanks. In addition to the above, an attempt was made to consider a large variety of weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were the collector area, storage tank heat loss coefficient (U-value), tank type, storage volume, type of system, and ten readings from real experiments of total daily solar radiation, mean ambient air temperature, and the Water temperature in the storage tank at the beginning of a day. The network output is the useful energy extracted from the system and the temperature rise in the stored Water. The 2 statistical R -value obtained for the training data set was equal to 0.9722 and 0.9751 for the two output parameters respectively. Unknown data were subsequently used to investigate the accuracy of prediction. These include systems considered for the training of the network at different weather conditions and completely unknown systems. Predictions within 7.1% and 9.7% were obtained respectively. These results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored Water. The advantages of this approach compared to the conventional algorithmic methods are the speed, the simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network. Additionally, actual weather data have been used for the training of the network, which leads to more realistic results as compared to other modeling programs, which rely on TMY data that are not necessarily similar to the actual environment in which a system operates. © 1999 Elsevier Science Ltd. All rights reserved.

J.h. Davidson - One of the best experts on this subject based on the ideXlab platform.

Marthinus Johannes Booysen - One of the best experts on this subject based on the ideXlab platform.

  • A potential source of undiagnosed Legionellosis: Legionella growth in Domestic Water Heating systems in South Africa
    Energy for Sustainable Development, 2019
    Co-Authors: Wendy Stone, Tobias M Louw, Godfrey Gakingo, Martin J Nieuwoudt, Marthinus Johannes Booysen
    Abstract:

    Abstract Legionella is a genus of pathogenic bacterial mesophiles that cause a range of diseases collectively referred to as Legionellosis, with immuno-compromised individuals being particularly susceptible. Water heaters, a potential Domestic niche for these pathogens, are heavy energy consumers, causing cost-sensitive users to employ energy-saving initiatives, such as scheduling and lower temperature set points. However, lower Water temperatures allow Legionella to flourish. This paper uses computational fluid dynamics modelling to show that a horizontal electric Water heater provides an environment that is conducive to Legionella growth, although its prevalence is probably higher in the downstream pipes. The presence of Legionella in Water heaters is established through Water sampled from five in-field Water heaters, of which the temperatures and Heating schedules are known. Microbiological techniques (PCR and weight-based qRT-PCR) are used to assess the presence of Legionella and L. pneumophila at point-of-use taps. A model is used to determine the potential infection rate from these concentrations, demonstrating that undiagnosed Legionellosis infection is likely. In low- and middle-income countries, like South Africa, misdiagnosis of Legionellosis may be common due to the shadow cast by HIV and TB prevalence.

  • A potential source of undiagnosed Legionellosis: Legionella growth in Domestic Water Heating systems in South Africa
    2019
    Co-Authors: Wendy Stone, Tobias M Louw, Godfrey Gakingo, Martin J Nieuwoudt, Marthinus Johannes Booysen
    Abstract:

    Legionella is a genus of pathogenic bacterial mesophiles that cause a range of diseases collectively referred to as Legionellosis, with immuno-compromised individuals being particularly susceptible. Water heaters, a potential Domestic niche for these pathogens, are heavy energy consumers, causing cost-sensitive users to employ energy-saving initiatives, such as scheduling and lower temperature set points. However, lower Water temperatures allow Legionella to flourish. This paper uses computational fluid dynamics modelling to show that a horizontal electric Water heater provides an environment that is conducive to Legionella growth, although its prevalence is probably higher in the downstream pipes. The presence of Legionella in Water heaters is established through Water sampled from five in-field Water heaters, of which the temperatures and Heating schedules are known. Microbiological techniques (PCR and weight-based qRT-PCR) are used to assess the presence of Legionella and Legionella pneumophila at point-of-use taps. A model is used to determine the potential infection rate from these concentrations, demonstrating that undiagnosed Legionellosis infection is likely. In low- and middle-income countries, like South Africa, misdiagnosis of Legionellosis may be common due to the shadow cast by HIV and TB prevalence.

S D Panteliou - One of the best experts on this subject based on the ideXlab platform.

  • thermosiphon solar Domestic Water Heating systems long term performance prediction using artificial neural networks
    Solar Energy, 2000
    Co-Authors: Soteris A. Kalogirou, S D Panteliou
    Abstract:

    Abstract The objective of this work is to use artificial neural networks (ANN) for the long-term performance prediction of thermosiphonic type solar Domestic Water Heating (SDWH) systems. Thirty SDWH systems have been tested and modelled according to the procedures outlined in the standard ISO 9459-2 at three locations in Greece. From these, data from 27 of the systems were used for training and testing the network while data from the remaining three were used for validation. Two ANNs have been trained using the monthly data produced by the modeling program supplied with the standard ISO 9459-2. Different networks were used depending on the nature of the required output, which is different in each case. The first network was trained to estimate the solar energy output of the system for a draw-off quantity equal to the storage tank capacity (at the end of the solar energy collection period) and the second one was trained to estimate the solar energy output of the system and the average quantity of hot Water per month at demand temperatures of 35 and 40°C. The collector areas of the considered systems were varying between 1.81 m2 and 4.38 m2. Open and closed thermosiphonic systems have been considered both with horizontal and vertical storage tanks. In this way the networks were trained to accept and handle a number of unusual cases. The input data in both networks are similar to the ones used in the program supplied with the standard. These were the size and performance characteristics of each system and various climatic data. In the second network the demand temperature was also used as input. For the first network the statistical coefficient of multiple determination (R2-value) obtained for the training data set was equal to 0.9993. For the second network the R2-value for the two output parameters was equal to 0.9848 and 0.9926, respectively. Unknown data were subsequently used to investigate the accuracy of prediction and R2-values equal to 0.9913 for the first network and 0.9733 and 0.9940 for the second were obtained. These results indicate that the proposed method can successfully be used for the prediction of the solar energy output of the system for a draw-off equal to the volume of the storage tank or for the solar energy output of the system and the average quantity of the hot Water per month for the two demand Water temperatures considered.

  • modeling of solar Domestic Water Heating systems using artificial neural networks
    Solar Energy, 1999
    Co-Authors: Soteris A. Kalogirou, S D Panteliou, A J Dentsoras
    Abstract:

    Artificial Neural Networks (ANN) are widely accepted as a technology offering an alternative way to tackle complex and ill-defined problems. They can be trained to predict results from examples, are fault tolerant, are able to deal with non-linear problems, and once trained can perform prediction at high speed. ANNs have been used in diverse applications and they have shown to be particularly useful in system modeling and for system identification. The objective of this work was to train an ANN to learn to predict the useful energy extracted and the temperature rise in the stored Water of solar Domestic Water Heating (SDHW) systems with the minimum of input data. An ANN has been trained based on 30 known cases of systems, 22 varying from collector areas between 1.81 m and 4.38 m . Open and closed systems have been considered both with horizontal and vertical storage tanks. In addition to the above, an attempt was made to consider a large variety of weather conditions. In this way the network was trained to accept and handle a number of unusual cases. The data presented as input were the collector area, storage tank heat loss coefficient (U-value), tank type, storage volume, type of system, and ten readings from real experiments of total daily solar radiation, mean ambient air temperature, and the Water temperature in the storage tank at the beginning of a day. The network output is the useful energy extracted from the system and the temperature rise in the stored Water. The 2 statistical R -value obtained for the training data set was equal to 0.9722 and 0.9751 for the two output parameters respectively. Unknown data were subsequently used to investigate the accuracy of prediction. These include systems considered for the training of the network at different weather conditions and completely unknown systems. Predictions within 7.1% and 9.7% were obtained respectively. These results indicate that the proposed method can successfully be used for the estimation of the useful energy extracted from the system and the temperature rise in the stored Water. The advantages of this approach compared to the conventional algorithmic methods are the speed, the simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network. Additionally, actual weather data have been used for the training of the network, which leads to more realistic results as compared to other modeling programs, which rely on TMY data that are not necessarily similar to the actual environment in which a system operates. © 1999 Elsevier Science Ltd. All rights reserved.

A Chaker - One of the best experts on this subject based on the ideXlab platform.

  • simulation of a solar Domestic Water Heating system
    Energy Procedia, 2011
    Co-Authors: I Zeghib, A Chaker
    Abstract:

    Abstract This paper shows the modelling of a Domestic solar Water Heating installation. The results of simulations performed on daily basis for a solar system (collector with surface of 2 m 2 and a storage tank of 200 litres), operated in Constantine (Algeria), which provides hot Water for Heating. The installation consists in a solar flat collector, a Water storage tank, a source of auxiliary energy and radiators. We analyse more accurately the influence of the thermosiphon-flow rate and consequently the stratification degree of the tank on the Water Heating system performances. The interest of this study resides in the approach used to model the tank and in the analysis of the number of the nodes used on the gained energy.