OriginalResearchPaper Received03June2016 Accepted1August2016 AvailableOnline02October2016 In this... more OriginalResearchPaper Received03June2016 Accepted1August2016 AvailableOnline02October2016 In this study, after fabricating a solar parabolic water heater, an efficient model is suggested to predict the efficiency of the solar water heater system (SWHS). Artificial neural networks (ANN) can create logical relations among the input parameters and target(s). The Efficiency is trained as a function of the input parameters, when conditions are desirable to measure the data, a network-trained function can be used to predict the efficiency of the solar system. The used data for the neural network analysis were measured by using experiments on a parabolic trough collector during four days in June. Variables such as solar radiation, ambient temperature and the output fluid temperature of the collector were considered as input parameters and the efficiency of the solar parabolic water heater was used as the output neural network. Different ANN models are presented based on the various input p...
Improving and enhancing methodologies for efficiently and effectively design of the energy system... more Improving and enhancing methodologies for efficiently and effectively design of the energy systems is one of the most important challenges that energy engineers face. In this work, a multi-objective particle swarm optimization algorithm is applied for a highly constrained cogeneration problem named CGAM problem as a standard cycle to verify all optimization methods. The regarded objective functions are the exergetic efficiency that should be maximized and the total cost rate that should be minimized, simultaneously. In order to determine the polar effects of the pressure ratio and the turbine inlet temperature on the specified objective functions, a sensitivity analysis is performed. The related Pareto fronts with different values of equivalence ratios, unit costs of fuel and NOx emissions are represented and their effects on the system are studied. Furthermore, the comparison of the obtained results with those of other evolutionary algorithms demonstrates the superiority and effici...
In the present study, two solar heating systems, including flat plate solar collector (FPC) and p... more In the present study, two solar heating systems, including flat plate solar collector (FPC) and parabolic solar collector (PTC) for heating a room were experimentally studied and compared with each other. For doing this, an experiment was conducted in the winter and the performance of these two systems was first measured and then compared with each other. It should be mentioned that the PTC system was manufactured and tested in this study. The ambient temperature, solar radiation intensity, and working fluid temperature in different locations of the two heating systems were measured. The results showed that the PTC solar heating system has higher collector and total efficiencies compared to the FPC heating system. The total efficiencies of the FPC and PTC systems were 6% and 12%, respectively. It was also indicated that the PTC system with lower occupied space can produce higher thermal energy quality compared to the FPC heating system. It was also concluded that the PTC solar heati...
An Artificial Neural Network (ANN) model based on PSO-GA optimization algorithm is applied to pre... more An Artificial Neural Network (ANN) model based on PSO-GA optimization algorithm is applied to predict a Solar Space Heating System (SSHS) performance. An experimental research is conducted into the SSHS equipped with a Parabolic Through Collector (PTC). A number of influential factors such as I, T a , T oc and T w are considered to validate the ANN results. The proposed PSO-GA algorithm is used to identify a complex non-linear relationship between input and output parameters of the SSHS, and to obtain the optimized estimating ANN model. To show the accuracy of the PSO-GA model in training ANN, its results are compared with those of two highly powerful optimization algorithms, namely High Exploration Particle Swarm Optimization (HEPSO) and Team Game Algorithm (TGA), based on some evaluating criteria such as Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), the coefficient of determination (R 2) and Root Mean Square Error (RMSE). Results show the reliability of PSO-GA-ANN with highest R 2 and RMSE. Afterwards, testing phases of the PSO-GA algorithm are done based on its parameters to obtain the best model of the neural network. Eventually, comparing the proposed model to Multi-Layer Perception Artificial Neural Network (MLPANN) exhibits its higher ability to estimate outputs when more accurate results are required.
In this study, several operational parameters of a solar energy system are predicted through usin... more In this study, several operational parameters of a solar energy system are predicted through using a multistage ANN model. To achieve the best design of this model, three different back-propagation learning algorithms, i.e. LevenbergMarquardt (LM), Pola-Riber Conjugate Gradient (CGP) and the Scaled Conjugate Gradient (SCG) are utilized. Further, to validate the ANN results, some experimental tests have been done in winter 2016 on a solar space heating system (SSHS) equipped with a parabolic trough collector (PTC). In the proposed model, ANN comprises three consecutive stages, while the outputs of each one are considered to be the inputs of the next. Results show that the maximum error rate in Stages 1, 2, and 3 has occurred in the LM algorithm with respectively 10, 6, and 10 neurons. Moreover, the best obtained determination coefficient of all stages belongs to the total system efficiency and has the value 0.999934 for LM-10. As a result, the multistage ANN model can simply forecast...
Oil is one of the most precious source of energy for the world and has an important role in the g... more Oil is one of the most precious source of energy for the world and has an important role in the global economy. Therefore, the long-term prediction of the crude oil price is an important issue in economy and industry especially in recent years. The purpose of this paper is introducing a new Particle Swarm Optimization (PSO) algorithm to forecast the oil prices. Indeed, the PSO is a population-based optimization method inspired by the flocking behavior of birds. Its original version suffers from tripping in local minima. Here, the PSO is enhanced utilizing a convergence operator, an adaptive inertia weight and linear acceleration coefficients. The numerical results of mathematical test functions, obtained by the proposed algorithm and other variants of the PSO elucidate that this new approach operates competently in terms of the convergence speed, global optimality and solution accuracy. Furthermore, the effective variables on the long-term crude oil price are regarded and utilized a...
OriginalResearchPaper Received03June2016 Accepted1August2016 AvailableOnline02October2016 In this... more OriginalResearchPaper Received03June2016 Accepted1August2016 AvailableOnline02October2016 In this study, after fabricating a solar parabolic water heater, an efficient model is suggested to predict the efficiency of the solar water heater system (SWHS). Artificial neural networks (ANN) can create logical relations among the input parameters and target(s). The Efficiency is trained as a function of the input parameters, when conditions are desirable to measure the data, a network-trained function can be used to predict the efficiency of the solar system. The used data for the neural network analysis were measured by using experiments on a parabolic trough collector during four days in June. Variables such as solar radiation, ambient temperature and the output fluid temperature of the collector were considered as input parameters and the efficiency of the solar parabolic water heater was used as the output neural network. Different ANN models are presented based on the various input p...
Improving and enhancing methodologies for efficiently and effectively design of the energy system... more Improving and enhancing methodologies for efficiently and effectively design of the energy systems is one of the most important challenges that energy engineers face. In this work, a multi-objective particle swarm optimization algorithm is applied for a highly constrained cogeneration problem named CGAM problem as a standard cycle to verify all optimization methods. The regarded objective functions are the exergetic efficiency that should be maximized and the total cost rate that should be minimized, simultaneously. In order to determine the polar effects of the pressure ratio and the turbine inlet temperature on the specified objective functions, a sensitivity analysis is performed. The related Pareto fronts with different values of equivalence ratios, unit costs of fuel and NOx emissions are represented and their effects on the system are studied. Furthermore, the comparison of the obtained results with those of other evolutionary algorithms demonstrates the superiority and effici...
In the present study, two solar heating systems, including flat plate solar collector (FPC) and p... more In the present study, two solar heating systems, including flat plate solar collector (FPC) and parabolic solar collector (PTC) for heating a room were experimentally studied and compared with each other. For doing this, an experiment was conducted in the winter and the performance of these two systems was first measured and then compared with each other. It should be mentioned that the PTC system was manufactured and tested in this study. The ambient temperature, solar radiation intensity, and working fluid temperature in different locations of the two heating systems were measured. The results showed that the PTC solar heating system has higher collector and total efficiencies compared to the FPC heating system. The total efficiencies of the FPC and PTC systems were 6% and 12%, respectively. It was also indicated that the PTC system with lower occupied space can produce higher thermal energy quality compared to the FPC heating system. It was also concluded that the PTC solar heati...
An Artificial Neural Network (ANN) model based on PSO-GA optimization algorithm is applied to pre... more An Artificial Neural Network (ANN) model based on PSO-GA optimization algorithm is applied to predict a Solar Space Heating System (SSHS) performance. An experimental research is conducted into the SSHS equipped with a Parabolic Through Collector (PTC). A number of influential factors such as I, T a , T oc and T w are considered to validate the ANN results. The proposed PSO-GA algorithm is used to identify a complex non-linear relationship between input and output parameters of the SSHS, and to obtain the optimized estimating ANN model. To show the accuracy of the PSO-GA model in training ANN, its results are compared with those of two highly powerful optimization algorithms, namely High Exploration Particle Swarm Optimization (HEPSO) and Team Game Algorithm (TGA), based on some evaluating criteria such as Mean Absolute Percentage Error (MAPE), Variance Accounted For (VAF), the coefficient of determination (R 2) and Root Mean Square Error (RMSE). Results show the reliability of PSO-GA-ANN with highest R 2 and RMSE. Afterwards, testing phases of the PSO-GA algorithm are done based on its parameters to obtain the best model of the neural network. Eventually, comparing the proposed model to Multi-Layer Perception Artificial Neural Network (MLPANN) exhibits its higher ability to estimate outputs when more accurate results are required.
In this study, several operational parameters of a solar energy system are predicted through usin... more In this study, several operational parameters of a solar energy system are predicted through using a multistage ANN model. To achieve the best design of this model, three different back-propagation learning algorithms, i.e. LevenbergMarquardt (LM), Pola-Riber Conjugate Gradient (CGP) and the Scaled Conjugate Gradient (SCG) are utilized. Further, to validate the ANN results, some experimental tests have been done in winter 2016 on a solar space heating system (SSHS) equipped with a parabolic trough collector (PTC). In the proposed model, ANN comprises three consecutive stages, while the outputs of each one are considered to be the inputs of the next. Results show that the maximum error rate in Stages 1, 2, and 3 has occurred in the LM algorithm with respectively 10, 6, and 10 neurons. Moreover, the best obtained determination coefficient of all stages belongs to the total system efficiency and has the value 0.999934 for LM-10. As a result, the multistage ANN model can simply forecast...
Oil is one of the most precious source of energy for the world and has an important role in the g... more Oil is one of the most precious source of energy for the world and has an important role in the global economy. Therefore, the long-term prediction of the crude oil price is an important issue in economy and industry especially in recent years. The purpose of this paper is introducing a new Particle Swarm Optimization (PSO) algorithm to forecast the oil prices. Indeed, the PSO is a population-based optimization method inspired by the flocking behavior of birds. Its original version suffers from tripping in local minima. Here, the PSO is enhanced utilizing a convergence operator, an adaptive inertia weight and linear acceleration coefficients. The numerical results of mathematical test functions, obtained by the proposed algorithm and other variants of the PSO elucidate that this new approach operates competently in terms of the convergence speed, global optimality and solution accuracy. Furthermore, the effective variables on the long-term crude oil price are regarded and utilized a...
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