Future projection of the energy dependency of Turkey using artificial neural network

Energy dependency (ED) implies the extent to which an economy relies upon imports in order to meet its energy needs. The ED is calculated as net imports divided by the sum of gross inland energy consumption plus bunkers. This study aims at obtaining numerical equations to estimate of Turkey's energy dependency based on basic energy indicators and sectoral energy consumption by using artificial neural network (ANN) technique. It seeks to contribute to the strategies necessary to preserve the supply–demand balance of Turkey. For this purpose, two different models were used to train the ANN approach. In Model 1, main energy indicators such as total production of primary energy per capita, total gross electricity generation per capita and final energy consumption per capita were used in the input layer of the ANN while sectoral energy consumption per capita was used in Model 2.The ED was in the output layer for both models. Different models were employed to estimate the ED with a high confidence for future projections. The R² values of ED were found to be 0.999 for both models. In accordance with the analysis results, ED is expected to increase from 72% to 82% within 14 years of period. Consequently, the utilization of renewable energy sources and nuclear energy is strictly recommended to ensure the ED stability in Turkey.     

Proton exchange membrane fuel cell performance prediction using artificial neural network

Polarization curves remain one of the parameters used to check the performance of fuels in terms of efficiency and durability. This investigation explores the application of artificial neutral network (ANN) to determine the voltage and current from a proton exchange membrane fuel cell having membrane area of 11.46 cm². Performance predictability for the group method of data handling (GMDH) as well as feed forward back propagation (FFBP) neutral networks were employed for the estimation of the current and voltage obtained from the Proton Exchange Membrane Fuel cell under investigation. The investigation presented models with good predictions even though GMDH neural network performed better than the FFBP neural network. The study therefore proposes the GMDH neural network as the best model for predicting the performance of a Proton Exchange Membrane Fuel cell. It was further deduced that an increase in reactant flow rate has direct effect on the performance of the fuel cell but this is directly proportional to the total irreversibilities in the cell hence to operate fuel cell economically, it is imperative that the hydrogen flow is made lower compare to the oxygen flow rate. This in effect will reduce the pumping power required for the flow of the fuel hence reducing the net loss in the cell.     

Modeling of solar radiation using remote sensing and artificial neural network in Turkey

Artificial neural networks (ANNs) were used to estimate solar radiation in Turkey (26–45°E, 36–42°N) using geographical and satellite-estimated data. In order to train the Generalized regression neural network (GRNN) geographical and satellite-estimated data for the period from January 2002 to December 2002 from 19 stations spread over Turkey were used in training (ten stations) and testing (nine stations) data. Latitude, longitude, altitude, surface emissivity for ?_4, surface emissivity for ?_5, and land surface temperature are used in the input layer of the network. Solar radiation is the output. Root Mean Square Error (RMSE) and correlation coefficient (R²) between the estimated and measured values for monthly mean daily sum with ANN values have been found as 0.1630 MJ/m² and 95.34% (training stations), 0.3200 MJ/m² and 93.41% (testing stations), respectively. Since these results are good enough it was concluded that the developed GRNN tool can be used to predict the solar radiation in Turkey.     

Performance prediction of wet cooling tower using artificial neural network under cross-wind conditions

This paper describes an application of artificial neural networks (ANNs) to predict the thermal performance of a cooling tower under cross-wind conditions. A lab experiment on natural draft counter-flow wet cooling tower is conducted on one model tower in order to gather enough data for training and prediction. The output parameters with high correlation are measured when the cross-wind velocity, circulating water flow rate and inlet water temperature are changed, respectively. The three-layer back propagation (BP) network model which has one hidden layer is developed, and the node number in the input layer, hidden layer and output layer are 5, 6 and 3, respectively. The model adopts the improved BP algorithm, that is, the gradient descent method with momentum. This ANN model demonstrated a good statistical performance with the correlation coefficient in the range of 0.993–0.999, and the mean square error (MSE) values for the ANN training and predictions were very low relative to the experimental range. So this ANN model can be used to predict the thermal performance of cooling tower under cross-wind conditions, then providing the theoretical basis on the research of heat and mass transfer inside cooling tower under cross-wind conditions.     

Performance prediction of PEM fuel cell with wavy serpentine flow channel by using artificial neural network

Effects of serpentine flow channel having sinusoidal wave at the rib surface on performance of PEMFC having 25 cm² active area are investigated at different flow rates, three different amplitudes changing from 0.25 mm to 0.75 mm and three different cell operation temperatures. A proton exchange membrane fuel cell (PEMFC) is modeled for the prediction of the output current by using artificial neural network (ANN) that is utilized the aforementioned experimental parameters. Effect of hydrogen and air flow rate, the fuel cell temperature, amplitude of channel is tested. The results indicated that model C1 having lowest amplitude is enhanced maximum power output up to 20.15% as compared to indicated conventional serpentine channel (model C4) for 0.7 SLPM H₂ and 1.5 SLPM air and also model C1 has better performance than C2, C3 and C4 models. The maximum power output is augmented with increasing the cell temperature due to raising the fuel and oxidant diffusion ratio. Cell temperature, amplitude, H₂ and air flow rate and input voltage is used as input variables in train and test of the developing ANN model. MAPE of training and testing is determined as 2.89 and 2.059, respectively. Prediction results of developed ANN model including two hidden layer shows similar trend with experimental results. Developed ANN model can be used to both decrease the number of required experiments and find the optimum operation condition within the range of input parameters.     

Greek long-term energy consumption prediction using artificial neural networks

In this paper artificial neural networks (ANN) are addressed in order the Greek long-term energy consumption to be predicted. The multilayer perceptron model (MLP) has been used for this purpose by testing several possible architectures in order to be selected the one with the best generalizing ability. Actual recorded input and output data that influence long-term energy consumption were used in the training, validation and testing process. The developed ANN model is used for the prediction of 2005–2008, 2010, 2012 and 2015 Greek energy consumption. The produced ANN results for years 2005–2008 were compared with the results produced by a linear regression method, a support vector machine method and with real energy consumption records showing a great accuracy. The proposed approach can be useful in the effective implementation of energy policies, since accurate predictions of energy consumption affect the capital investment, the environmental quality, the revenue analysis, the market research management, while conserve at the same time the supply security. Furthermore it constitutes an accurate tool for the Greek long-term energy consumption prediction problem, which up today has not been faced effectively.     

Wind turbine power curve modelling using artificial neural network

Technical improvements over the past decade have increased the size and power output capacity of wind power plants. Small increases in power performance are now financially attractive to owners. For this reason, the need for more accurate evaluations of wind turbine power curves is increasing. New investigations are underway with the main objective of improving the precision of power curve modeling. Due to the non-linear relationship between the power output of a turbine and its primary and derived parameters, Artificial Neural Network (ANN) has proven to be well suited for power curve modelling. It has been shown that a multi-stage modelling techniques using multilayer perceptron with two layers of neurons was able to reduce the level of both the absolute and random error in comparison with IEC methods and other newly developed modelling techniques. This newly developed ANN modeling technique also demonstrated its ability to simultaneously handle more than two parameters. Wind turbine power curves with six parameters have been modelled successfully. The choice of the six parameters is crucial and has been selected amongst more than fifty parameters tested in term of variability in differences between observed and predicted power output. Further input parameters could be added as needed.     

Daily solar irradiation estimation over a mountainous area using artificial neural networks

In order to design both active and passive solar energy systems, radiation data are needed for the studied location. The implementation of such renewable energy systems is especially important in places like natural parks, where acoustic and fossil fuel derived contamination has to be completely avoided. Measure of solar radiation is usually accomplished by means of radiometric station nets with a low spatial resolution. To estimate the radiation in sites located away from the stations, different interpolation/extrapolation techniques may be used. These methods are valid on places where the spatial variability of radiation is not significant, but becomes less accurate if complex terrain areas are present in between the radiometric stations. As an alternative, artificial intelligence techniques have been used in this work, along with a 20 m resolution digital model of terrain. The inputs to the network have been selected using the automatic relevance determination methodology. The data set contains 3 years’ data of daily global radiation measured at 12 different stations located in the north face of the Sierra Nevada National Park in the surroundings of Huéneja (Granada), a town located in the South East of Spain. The stations altitude varies from 1000 to 1700 m. The goal of this work has been to estimate daily global irradiation on stations located in a complex terrain, and the values estimated by the neural network model have been compared with the measured ones leading to a root mean square error (RMSE) of 6.0% and a mean bias error (MBE) of 0.2%, both expressed as a percentage of the mean value. Performance achieved individually for each of the stations lies in the range [5.0–7.5]% for the RMSE and [−1.2 to +2.1]% for the MBE. Results point out artificial neural networks as an efficient and easy methodology for calculating solar radiation levels over complex mountain terrains from only one radiometric station data. In addition, this methodology can be applied to other areas with a complex topography.     

Energy demand estimation of South Korea using artificial neural network

Because South Korea's industries depend heavily on imported energy sources (fifth largest importer of oil and second largest importer of liquefied natural gas in the world), the accurate estimating of its energy demand is critical in energy policy-making. This research proposes an artificial neural network model (a structure with feed-forward multilayer perceptron, error back-propagation algorithm, momentum process, and scaled data) to efficiently estimate the energy demand for South Korea. The model has four independent variables, such as gross domestic product (GDP), population, import, and export amounts. The data are obtained from diverse local and international sources. The proposed model better estimated energy demand than a linear regression model (a structure with multiple linear variables and least square method) or an exponential model (a structure with mixed integer variables, branch and bound method, and Broyden–Fletcher–Goldfarb–Shanno (BFGS) method) in terms of root mean squared error (RMSE). The model also forecasted better than the other two models in terms of RMSE without any over-fitting problem. Further testing with four scenarios based upon reliable source data showed unanticipated results. Instead of growing permanently, the energy demands peaked at certain points, and then decreased gradually. This trend is quite different from the results by regression or exponential model.     

Estimating global solar radiation using artificial neural network and air temperature data in a semi-arid environment

Global solar radiation (GSR) data are desirable for many areas of research and applications in various engineering fields. However, GSR is not as readily available as air temperature data. Artificial neural networks (ANNs) are effective tools to model nonlinear systems and require fewer inputs. The objective of this study was to test an artificial neural network (ANN) for estimating the global solar radiation (GSR) as a function of air temperature data in a semi-arid environment. The ANNs (multilayer perceptron type) were trained to estimate GSR as a function of the maximum and minimum air temperature and extraterrestrial radiation. The data used in the network training were obtained from a historical series (1994–2001) of daily climatic data collected in weather station of Ahwaz located in Khuzestan plain in the southwest of Iran. The empirical Hargreaves and Samani equation (HS) is also considered for the comparison. The HS equation calibrated by applying the same data used for neural network training. Two historical series (2002–2003) were utilized to test the network and for comparison between the ANN and calibrated HS method. The study demonstrated that modelling of daily GSR through the use of the ANN technique gave better estimates than the HS equation. RMSE and R² for the comparison between observed and estimated GSR for the tested data using the proposed ANN model are 2.534 MJ m^?2 day^?1 and 0.889 respectively.     

State-of-charge prediction of batteries and battery-supercapacitor hybrids using artificial neural networks

The state-of-charge (SOC) of batteries and battery-supercapacitor hybrid systems is predicted using artificial neural networks (ANNs). Our technique is able to predict the SOC of energy storage devices based on a short initial segment (less than 4% of the average lifetime) of the discharge curve. The prediction shows good performance with a correlation coefficient above 0.95. We are able to improve the prediction further by considering readily available measurements of the device and usage. The prediction is further shown to be resilient to changes in operating conditions or physical structure of the devices.     

The cycle life prediction of Mg-based hydrogen storage alloys by artificial neural network

Mg-based hydrogen storage alloys are a type of promising cathode material of Nickel-Metal Hydride (Ni-MH) batteries. But inferior cycle life is their major shortcoming. Many methods, such as element substitution, have been attempted to enhance its life. However, these methods usually require time-consuming charge–discharge cycle experiments to obtain a result. In this work, we suggested a cycle life prediction method of Mg-based hydrogen storage alloys based on artificial neural network, which can be used to predict its cycle life rapidly with high precision. As a result, the network can accurately estimate the normalized discharge capacities vs. cycles (after the fifth cycle) for Mg_0.8Ti_0.1M_0.1Ni (M = Ti, Al, Cr, etc.) and Mg_0.9 − xTi_0.1Pd_xNi (x = 0.04–0.1) alloys in the training and test process, respectively. The applicability of the model was further validated by estimating the cycle life of Mg_0.9Al_0.08Ce_0.02Ni alloys and Nd₅Mg₄₁–Ni composites. The predicted results agreed well with experimental values, which verified the applicability of the network model in the estimation of discharge cycle life of Mg-based hydrogen storage alloys.     

应用神经网络方法预测生物质的热值

应用人工神经网络方法对生物质的热值进行了预测,网络的训练数据集来自美国Biomass Feedstock Composition and Property Database of U．S．Department of Energy。神经网络以生物质的工业分析结果作为输入数据．采用56组数据对网络进行训练,以7组数据对网络进行验证,对网络输出值与实际值进行比较,相对误差在0．08％以内。人工神经网络成功地预测各种生物质的热值,说明人工神经网络能够处理生物质的热值与工业分析各组分间的非线性关系。     

Application of artificial neural network method for performance prediction of a gas cooler in a CO2 heat pump

The objective of this work is to train an artificial neural network (ANN) to predict the performance of gas cooler in carbon dioxide transcritical air-conditioning system. The designed ANN was trained by performance test data under varying conditions. The deviations between the ANN predicted and measured data are basically less than ±5%. The well-trained ANN is then used to predict the effects of the five input parameters individually. The predicted results show that for the heat transfer and CO₂ pressure drop the most effective factor is the inlet air velocity, then come the inlet CO₂ pressure and temperature. The inlet mass flow rate can enhance heat transfer with a much larger CO₂ pressure drop penalty. The most unfavorable factor is the increase in the inlet air temperature, leading to the deterioration of heat transfer and severely increase in CO₂ pressure drop.     

基于人工神经网络和模拟集成的短期光伏发电预测

文章提出了一种基于人工神经网络(ANN)和模拟集成(AnEn)的短期光伏发电预测方法。该方法首先利用数值天气预报模型,以计算天文变量为输入,对光伏发电站点进行72 h的确定性和概率预测;然后分别运用基于ANN,AnEn和ANN+AnEn联合模型方法对3个光伏发电站点进行预测,并进一步利用模拟4 450个光伏电站的综合数据验证了该模型方法的可扩展性;最后利用美国国家大气研究中心(NCAR)的黄石超级计算机,在1个节点(32核)～4 450个节点(141 140核)内测试了该方法的并行运算处理能力。实验结果表明,基于ANN+AnEn联合模型方法可以获得最佳结果,同时证明了该方法适用于大规模并行计算。     

Transport energy demand modeling of South Korea using artificial neural network

Artificial neural network models were developed to forecast South Korea's transport energy demand. Various independent variables, such as GDP, population, oil price, number of vehicle registrations, and passenger transport amount, were considered and several good models (Model 1 with GDP, population, and passenger transport amount; Model 2 with GDP, number of vehicle registrations, and passenger transport amount; and Model 3 with oil price, number of vehicle registrations, and passenger transport amount) were selected by comparing with multiple linear regression models. Although certain regression models obtained better R-squared values than neural network models, this does not guarantee the fact that the former is better than the latter because root mean squared errors of the former were much inferior to those of the latter. Also, certain regression model had structural weakness based on P-value. Instead, neural network models produced more robust results. Forecasted results using the neural network models show that South Korea will consume around 37 MTOE of transport energy in 2025.     

A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy

Forecasting of solar irradiance is in general significant for planning the operations of power plants which convert renewable energies into electricity. In particular, the possibility to predict the solar irradiance (up to 24 h or even more) can became - with reference to the Grid Connected Photovoltaic Plants (GCPV) - fundamental in making power dispatching plans and - with reference to stand alone and hybrid systems - also a useful reference for improving the control algorithms of charge controllers. In this paper, a practical method for solar irradiance forecast using artificial neural network (ANN) is presented. The proposed Multilayer Perceptron MLP-model makes it possible to forecast the solar irradiance on a base of 24 h using the present values of the mean daily solar irradiance and air temperature. An experimental database of solar irradiance and air temperature data (from July 1st 2008 to May 23rd 2009 and from November 23rd 2009 to January 24th 2010) has been used. The database has been collected in Trieste (latitude 45°40′N, longitude 13°46′E), Italy. In order to check the generalization capability of the MLP-forecaster, a K-fold cross-validation was carried out. The results indicate that the proposed model performs well, while the correlation coefficient is in the range 98-99% for sunny days and 94-96% for cloudy days. As an application, the comparison between the forecasted one and the energy produced by the GCPV plant installed on the rooftop of the municipality of Trieste shows the goodness of the proposed model.     

Thermosiphon solar domestic water heating systems: long-term performance prediction using artificial neural networks

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.     

基于PCA-Elman神经网络的短期PV/T组件温度预测

为了进一步提高光伏/光热一体化(PV/T)系统中PV/T组件温度的预测精度,使得PV/T系统能够根据PV/T组件温度的波动情况提前准确地做出控制决策,以优化控制效果,文章在分析PV/T组件温度与气象因素的相关性以及相邻时间序列温度自相关性的基础上,采用主成分分析法对原始输入样本数据进行预处理,并提取该样本数据的主成分,然后结合反馈型Elman神经网络理论,建立动态预测模型。研究结果表明,相对于未提取主成分的神经网络模型,提取主成分的神经网络模型的预测精度更高,泛化性能更强。     

Prediction of phase equilibria of HIx system using artificial neural network: Experimental verification

Thermochemical sulfur–iodine (SI) cycle is one of the promising technologies investigated for hydrogen production using solar and nuclear energy. The development and validation of a reliable thermodynamic model for the HIx mixture (HI–H₂O–I₂) encountered in the SI cycle have been identified as a central research issue to provide estimations on the HIx section energy demand.