Adnan Sözen

Use of artificial neural networks for mapping of solar potential in Turkey

Turkey has sufficient solar radiation intensities and radiation durations for solar thermal applications since Turkey lies in a sunny belt, between 36° and 42° N latitudes. The yearly average solar-radiation is 3.6 kWh/m2day, and the total yearly radiation period is ~2610 h. The main focus of this study is to determine the solar-energy potential in Turkey using artificial neural-networks (ANNs). Scaled conjugate gradient (SCG), Pola-Ribiere conjugate gradient (CGP), and Levenberg-Marquardt (LM) learning algorithms and a logistic sigmoid transfer function were used in the network. In order to train the neural network, meteorological data for the last 3 years (2000-2002) from 17 stations (namely cities) spread over Turkey were used as training (11 stations) and testing (6 stations) data. Meteorological and geographical data (latitude, longitude, altitude, month, mean sunshine duration, and mean temperature) are used as inputs to the network. Solar radiation is in the output layer. The maximum mean absolute percentage error was found to be less than 6.7% and R2values to be about 99.8937% for the testing stations. However, the respective values were found to be 2.41 and 99.99658% for the training stations. The trained and tested ANN models show greater accuracies for evaluating solar resource posibilities in regions where a network of monitoring stations has not been established in Turkey. The predicted solar-potential values from the ANN were given in the form of monthly maps. These maps are of prime importance for different working disciplines, like those of scientists, architects, meteorologists, and solar engineers in Turkey. The predictions from ANN models could enable scientists to locate and design solar-energy systems in Turkey and determine the appropriate solar technology.

A new approach to thermodynamic analysis of ejector–absorption cycle: artificial neural networks

Abstract Thermodynamic analysis of absorption thermal systems is too complex because of analytic functions calculating the thermodynamic properties of fluid couples involving the solution of complex differential equations. To simplify this complex process, the use of artificial neural networks (ANNs) has been proposed for the analysis of ejector–absorption refrigeration systems (EARSs). ANNs approach was used to determine the properties of liquid and two phase boiling and condensing of an alternative working fluid couple (methanol/LiBr), which does not cause ozone depletion for EARS. The back-propagation learning algorithm with three different variants and logistic sigmoid transfer function was used in the network. In addition, this paper presents a comparative performance study of the EARS using both analytic functions and prediction of ANN for properties of the fluid couple. After training, it was found that average error is less than 1.3% and R2 values are about 0.9999. Additionally, when the results of analytic equations obtained by using experimental data and by means of ANN were compared, deviations in coefficient of performance (COP), exergetic coefficient of performance (ECOP) and circulation ratio (F) for all working temperatures were found to be less than 1.8%, 4%, 0.2%, respectively. Deviations for COP, ECOP and F at a generator temperature of ∼90 °C for which the COP of the system is maximum are 1%, 2%, 0.1%, respectively, for other working temperatures. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable uncertainties.

Determination of efficiency of flat-plate solar collectors using neural network approach

In this study, a new formula based on artificial neural network (ANN) technique was developed to determine the efficiency of flat-plate solar collectors. In practice, the ANN model can be used for modeling the efficiency of solar collectors with complex structures when other models may have difficulties. Logistic sigmoid transfer function was used in the network. Meteorological data of summer session (from July to September) for Ankara were used as training data in order to train the neural network. Surface temperature in collector, date, time, solar radiation, declination angle, azimuth angle and tilt angle are used in the input layer of the network. The efficiency of flat-plate solar collector is in the output layer. The results show that the maximum and minimum deviations were found 2.558484 and 0.001969, respectively. The advantages of ANN model compared to the conventional testing methods are speed, simplicity, and the capacity of the ANN to learn from examples.

Effect of heat exchangers on performance of absorption refrigeration systems

Abstract In this study, the effect of heat exchangers on the system performance in an aqua–ammonia absorption refrigeration system has been investigated. For this purpose, the thermodynamic performance of the system is analysed, and the irreversibilities in the thermal processes have been determined for three different cases. These cases are: both of the heat exchangers are included in the system, only the refrigerant heat exchanger is included in the system and only the mixture heat exchanger is included in the system. It is assumed that the ammonia concentration at the rectifier outlet is independent of the other parameters, constant and equal to 0.999. The coefficient of performance, exergetic coefficient of performance, circulation ratio ( f ) and non-dimensional exergy loss of each component of the system are calculated separately. After these calculations, some graphics indicating the change of the variables with the system parameters have been plotted.

Development and testing of a prototype of absorption heat pump system operated by solar energy

Abstract In this study, a prototype of an aqua–ammonia absorption heat pump system (AHP) using solar energy was investigated. The performance tests of the system were performed for the climate condition of Ankara in Turkey. The system has been designed operating with a parabolic slote type collector to obtain the required temperatures. In the experiments, high temperature water obtained from the collector was used as heat source needed for the generator. The system design configuration was analysed by using the experimental data. The effect of irreversibilities in thermal process on the system performance in AHP were determined. Thermodynamic analysis shows that both losses and irreversibility have an impact on absorption system performance. The study indicates which components in the system need to be improved thermally. This study will contribute the development of the system for the future use of solar-powered food preservation and commercial air conditioning.

Prospects for Future Projections of the Basic Energy Sources in Turkey

Abstract The main goal of this study is to develop the energy sources estimation equations in order to estimate the future projections and make correct investments in Turkey using artificial neural network (ANN) approach. It is also expected that this study will be helpful in demonstrating energy situation of Turkey in amount of EU countries. Basic energy indicators such as population, gross generation, installed capacity, net energy consumption, import, export are used in input layer of ANN. Basic energy sources such as coal, lignite, fuel-oil, natural gas and hydraulic are in output layer. Data from 1975 to 2003 are used to train. Three years (1981, 1994 and 2003) are only used as test data to confirm this method. Also, in this study, the best approach was investigated for each energy sources by using different learning algorithms (scaled conjugate gradient [SCG] and Levenberg-Marquardt [LM]) and a logistic sigmoid transfer function in the ANN with developed software. The statistical coefficients of multiple determinations (R2-value) for training data are equal to 0.99802, 0.99918, 0.997134, 0.998831 and 0.995681 for natural gas, lignite, coal, hydraulic, and fuel-oil, respectively. Similarly, these values for testing data are equal to 0.995623, 0.999456, 0.998545, 0.999236, and 0.99002. The best approach was found for lignite by SCG algorithm with seven neurons so mean absolute percentage error (MAPE) is equal to 1.646753 for lignite. According to the results, the future projections of energy indicators using ANN technique have been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users.

Performance improvement of absorption refrigeration system using triple-pressure-level

Abstract In the absorption refrigeration system (ARS) working with aqua–ammonia, the ejector is commonly located at the condenser inlet. In this study, the ejector was located at the absorber inlet. Therefore, the absorber pressure becomes higher than the evaporator pressure and the system works with triple-pressure-level. The ejector has two main functions: (i) aiding pressure recovery from the evaporator, (ii) upgrading the mixing process and the pre-absorption by the weak solution of the ammonia coming from the evaporator. In addition to these functions, it can also act to lower the refrigeration and heat-source temperatures. Energy analyses show that the system’s coefficient of performance (COP) and exergetic coefficient of performance (ECOP) were improved by 49% and 56%, respectively and the circulation ratio (f) was reduced by 57% when ARS is initiated at lower generator temperatures. Due to the reduced circulation ratio, the system dimensions can be reduced; consequently, this decreases overall cost. The heat source and refrigeration temperatures decreased in the range of 5–15 °C and 1–3 °C, respectively. Exergy analyses show that the exergy loss of the absorber of ARS with ejector had a higher exergy loss than those of the other components. Therefore, a multiple compartment absorber can be proposed to reduce the exergy loss of the absorber of ARS with ejector.

Forecasting Net Energy Consumption Using Artificial Neural Network

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using the artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Logistic sigmoid transfer function was used in the network. In order to train the neural network, population, and gross generation, installed capacity and years is used in input layer of network. The net energy consumption is in output layer. The input values in 1965, 1981, and 1997 are only used as test data to confirm this method. The statistical coefficient of multiple determinations (R 2-value) is equal to 0.9999 and 1 for training and test data, respectively. According to the results, the NEC using the ANN technique has been obviously predicted within acceptable errors. Apart from reducing the whole time required, the importance of the ANN approach is possible to find solutions that make energy applications more viable and thus more attractive to potential users. It is also expected that this study will be helpful in developing highly applicable and productive planning for energy policies.

Determination of thermodynamic properties of an alternative refrigerant (R407c) using artificial neural network

Thermodynamic analysis of the refrigeration systems is too complex because of thermodynamic properties equations of working fluids, involving the solution of complex differential equations. To simplify this complex process, this paper proposes a new approach (artificial neural network, ANN) to determine of thermodynamic properties of an environmentally friendly alternative refrigerant (R407c) for both saturated liquid-vapor region (wet vapor) and superheated vapor region. Instead of complex rules and mathematical routines, ANNs are able to learn the key information patterns within multidimensional information domain. Therefore, reducing the risk of experimental uncertainties and also removing the need for complex analytic equations requiring long computational time and efforts. R^2 values - which are errors known as absolute fraction of variance - in wet vapor region are 0.999706, 0.999949, 0.999909, 0.999988 and 0.999836 for specific volume, enthalpy, entropy, viscosity and thermal conductivity for training, respectively. Similarly, for superheated vapor, they are: 0.99992, 1, 0.99998, 0.99995 and 0.99996 for training data, respectively. Promising thermodynamics property results have been obtained for R407c within acceptable errors. PVTx properties predicted are in valid region for working conditions of the refrigeration systems in case of use to computer simulation programs.

Modelling of Turkey's net energy consumption using artificial neural network

The main goal of this study is to develop the equations for forecasting net energy consumption (NEC) using artificial neural network (ANN) technique in order to determine the future level of the energy consumption in Turkey. Two different models were used in order to train the neural network: population, gross generation, installed capacity and years are used in input layer of network (Model 1); energy sources are used in input layer of network (Model 2). The NEC is in output layer for two models. R2 values for training data are equal to 0.99944 and 0.99913, for Model 1 and Model 2, respectively. Similarly, R2 values for testing data are equal to 0.997386 and 0.999558 for Model 1 and Model 2, respectively. According to the results, the NEC prediction using ANN technique will be helpful in developing highly applicable and productive planning for energy policies.