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.     

基于高阶BP神经网络的日最大负荷预测

随着社会的发展,人们的日常生活和工作生产越来越依赖于电力系统.精准的电力负荷预测是电网安全、稳定运行的重要保障.为减小节假日在日最大负荷预测过程中的影响,提出了法定节假日对日最大负荷的影响及日类型量化处理方法,并采用一种改进的BP(back propagation)神经网络——高阶BP神经网络进行连续多天最大负荷预测.实验算例结果表明:该数据处理和预测方法能有效地减小节假日对负荷预测的影响,提高了预测精度,并有较强的工程实践价值和应用前景.     

Wind speed prediction in the mountainous region of India using an artificial neural network model

Measured wind speed data are not available for most sites in the mountainous regions of India. The objective of present study is to predict wind speeds for 11 locations in the Western Himalayan Indian state of Himachal Pradesh to identify possible wind energy applications. An artificial neural network (ANN) model is used to predict wind speeds using measured wind data of Hamirpur location for training and testing. Temperature, air pressure, solar radiation and altitude are taken as inputs for the ANN model to predict daily mean wind speeds. Mean absolute percentage error (MAPE) and correlation coefficient between the predicted and measured wind speeds are found to be 4.55% and 0.98 respectively. Predicted wind speeds are found to range from 1.27 to 3.78 m/s for Bilaspur, Chamba, Kangra, Kinnaur, Kullu, Keylong, Mandi, Shimla, Sirmaur, Solan and Una locations. A micro-wind turbine is used to assess the wind power generated at these locations which is found to vary from 773.61 W to 5329.76 W which is suitable for small lighting applications. Model is validated by predicting wind speeds for Gurgaon city for which measured data are available with MAPE 6.489% and correlation coefficient 0.99 showing high prediction accuracy of the developed ANN Model.     

Application of artificial neural network for emission prediction of dust pollutants

We study the application of artificial neural network (ANN) for predicting suspended particulate concentrations in urban air, taking into account meteorological conditions. Calculations are based on pollution measurements taken in the city of Radom, Poland, in the period 2001–2002. PM10 emission and primary meteorological data, which were obtained from IEP in Radom, were used to train and test the application of network. Two different methods of emission calculation are applied. Firstly, ANN method based on multilayer perceptron with unidirectional information flow is used. Secondly, a hybrid model based on a modified Gaussian model of Pasquille's type and ANN with radial base function (RBF) is applied. Network architecture and transition function types are described. Statistical assessment of the obtained results is made. In addition, hybrid model results are compared with emission calculations of dust pollution based on the Gaussian model, including various methods of calculation of pollution dispersion coefficients. Copyright © 2006 John Wiley & Sons, Ltd.     

Electric load analysis using an artificial neural network

Load forecasting in the current, increasingly liberalized, electricity power market is of crucial importance as a means for producers to optimize and rationalize energy supply. A number of electric power companies are equipped to make forecasts with the aid of traditional statistical methods. This paper presents the use of an artificial neural net to an hourly based load forecasting application for a small electric grid on an Italian island (Lipari) not connected to the mainland. The aim was to examine the forecasting ability of a neural net in a situation where the electric load was subject to considerable seasonal variations over the year. The variations are affected by energy demand related to the tourism season as well as by climatic conditions, especially temperature. The network developed was a multi‐layer perceptron type built on three layers trained with a back‐propagation algorithm. The input layer receives all the most relevant information regarding: the class of day type, the hour in the daytime, the load and background temperature recorded at the indicated time for the months of March, August and October whilst the output layer provides the forecast value at the indicated time in December. The results obtained are encouraging; in the training phase the RMS error rate was around 2% and this rate settled at about 2.6% during testing. As both the margins of error recorded are acceptable, the use of a neural network for electric load forecasting applications can be considered an attractive option. Copyright © 2005 John Wiley & Sons, Ltd.     

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.     

Thermodynamic analysis of absorption systems using artificial neural network

Thermodynamic analysis of absorption systems is a very complex process, mainly because of the limited experimental data and analytical functions required for calculating the thermodynamic properties of fluid pairs, which usually involves the solution of complex differential equations. In order to simplify this complex process, Artificial Neural Networks (ANNs) are used. In this study, ANNs are used as a new approach for the determination of the thermodynamic properties of LiBr–water and LiCl–water solutions which have been the most widely used in the absorption heat pump systems. Instead of complex differential equations and limited experimental data, faster and simpler solutions were obtained by using equations derived from the ANN model. It was found that the coefficient of multiple determination (R²-value) between the actual and ANN predicted data is equal to about 0.999 for the enthalpy of both LiBr–water and LiCl–water solutions. As seen from the results obtained, the calculated thermodynamic properties are obviously within acceptable limits. In addition, the coefficient of performance (COP) of absorption systems operating under different conditions with LiBr–water and LiCl–water solutions is calculated. The use of the derived equations, which can be employed with any programming language or spreadsheet program for the estimation of the enthalpy of the solutions, as described in this paper, may make the use of dedicated ANN software unnecessary.     

Hybrid boosting algorithms and artificial neural network for wind speed prediction

Energy sources are an important foundation for national economic growth. The future of energy sources depend on the energy controls. The reserves of fossil energy have declined significantly, and environmental pollution has increased dramatically due to excessive fossil fuel consumption. At this point, wind energy can be used as one of the key source of renewable energy. It has a remarkable importance among the low-carbon energy technologies. The primary aim of wind energy production is to reduce dependence on fossil fuels that affect environment adversely. Therefore, wind energy is analyzed to develop new energy resources. The main issue related to evaluation of the wind energy potential is wind speed prediction. Due to the high volatile and irregular nature of wind speed, wind speed prediction is difficult. To cope with complex data structure, this study presents the development of extreme gradient boosting (XGBoost), adaptive boosting (AdaBoost), and artificial neural network (ANN) within particle swarm optimization (PSO) parameter optimization for hourly wind speed prediction. To compare the proposed hybrid methods, various performance measures, the Pearson's test, and the Taylor diagram are used. The results showed that proposed hybrid methods provide reasonable prediction results for wind speed prediction.     

Short‐term electric load forecasting using an artificial neural network: case of Northern Vietnam

Short‐term electric load forecasting is an important requirement for electric system operation. This paper employs a feed‐forward neural network with a back‐propagation algorithm for three types of short‐term electric load forecasting: daily peak (valley) load, hourly load and the total load. The forecast has been made for the northern areas of Vietnam using a large set of data on peak load, valley load, hourly load and temperature. The data were used to train and calibrate the artificial neural network, and the calibrated network was used for load forecasting. The results obtained from the model show that the application of neural network to short‐term electric load forecasting problem is very useful with quite accurate results. These results compare well with other similar studies. Copyright © 2004 John Wiley & Sons, Ltd.     

Application of artificial neural network in performance prediction of PEM fuel cell

Investigations on using artificial neural networks to predict the performance of single proton exchange membrane fuel cell has been carried out. Two sets of polarization data obtained at different temperatures and flow rates are used to create and simulate the network. Cell temperature, humidification temperatures, H₂/air flow rates and current density have been used as inputs, and voltage is used as observed (output) value to train and simulate the network. This nonlinear data are batch trained, and artificial neural network has been constructed using feed forward backpropagation algorithm. Performance of the training has been improved by increasing the number of neurons to reduce the error. Simulation results are in agreement with experimental data, and the corresponding networks are used to predict the polarization behavior for unknown inputs. Copyright © 2011 John Wiley & Sons, Ltd.     

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.     

Development and optimization of artificial neural network algorithms for the prediction of building specific local temperature for HVAC control

This research accounts for the outcome of a major cloud-based smart dual fuel switching system (SDFSS) project, which is a dual-fuel integrated hybrid heating, ventilation, and air conditioning (HVAC) system in residential homes. The SDFSS was developed to enable optimized, flexible, and cost-effective switching between the natural gas furnace and electric air source heat pump (ASHP). In order to meet the optimal energy consumption requirements in the house and provide thermal comfort for the residents, various high-quality sensors and meters were installed to record multiple data points inside and outside the house. The performance of the system was monitored in the long term, which is a common practice in energy monitoring projects. Outdoor temperature data plays the most crucial role in operating HVAC systems and also is a key variable in the decision-making algorithm of the SDFSS controller. Therefore, this study introduces an innovative and unique approach to obtain the outdoor temperature that could potentially replace high precision sensors with a data-driven model utilizing weather station data at a time resolution of 2 minutes and 1 hour. In this work, a series of artificial neural network algorithms were developed, optimized, and implemented to predict the outdoor temperature with an average of 0.99 coefficient of correlation (R), 1.011 mean absolute error (MAE), and 1.315 root mean square error (RMSE). It has been demonstrated that the developed ANN is a reliable and powerful tool in predicting outdoor temperature. Thus, the proposed model is strongly suggested to be implemented as an alternative to temperature sensors in hybrid energy systems or similar systems requiring accurate ambient temperature measurements.     

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.     

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.     

Modelling of a cascade refrigeration system using artificial neural network

This study investigates the applicability of artificial neural networks (ANNs) to predict various performance parameters of a cascade vapour compression refrigeration system. For this aim, an experimental cascade system was set up and tested in steady‐state operating conditions. Then, utilizing some of the experimental data for training, an ANN model for the system based on the standard back propagation algorithm was developed. The ANN was used for predicting the evaporating temperature in the lower‐temperature circuit, compressor power for the lower and higher circuits and coefficients of performance for both the lower circuit and the overall cascade system. Afterwards, the performances of the ANN predictions were tested using new experimental data. The ANN predictions usually agreed well with the experimental results with correlation coefficients in the range of 0.953–0.996 and mean relative errors in the range of 0.2–6.0%. Furthermore, the ANN yielded acceptable predictions for the system performance outside the range of the experiments. The results suggest that the ANN approach can alternatively and reliably be used for modelling cascade refrigeration systems. Copyright © 2006 John Wiley & Sons, Ltd.