Turkey's Forecasting of Energy Demand with Artificial Neural-Network (original) (raw)
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Forecasting Domestic Energy Demand in Turkey with an Artificial Neural Network Approach
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Forecasting Turkey’s Energy Demand Using Artificial Neural Networks: Three Scenario Applications
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Energy has become increasingly crucial for countries as we have experienced high economic growth, increases in population together with rapid urbanization in the globalized world. Turkey’s energy demand has grown rapidly and is expected to continue growing. In this context many studies have been carried out to forecast energy demand in Turkey. The energy demand forecasts are officially prepared by
As Turkey has limited energy resources and satisfies a large part of its energy needs using foreign resources, this study evaluates Turkey's current energy conditions and presents a set of energy projections to contribute to the country's future plans, programmes and policies. Based on the widespread view that the energy deficit is one of the most important constraints on Turkey's sustainable growth, this study presents a set of predictions covering Turkey's energy production and consumption for the nine-year period between 2012 and 2020. Based on the results, this study also proposes a set of solutions. In this study, a projection of Turkey's energy production is made by considering the energy production targets of existing plants and the new energy plants that are planned to be completed in the projected future. In addition, the energy demand is forecasted by using a type of artificial intelligence model known as an artificial neural network.
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Artificial Neural Networks are proposed to model and predict electricity consumption of Turkey. Multi layer perceptron with backpropagation training algorithm is used as the neural network topology. Tangent-sigmoid and pure-linear transfer functions are selected in the hidden and output layer processing elements, respectively. These input-output network models are a result of relationships that exist among electricity consumption and several other socioeconomic variables. Electricity consumption is modeled as a function of economic indicators such as population, gross national product, imports and exports. It is also modeled using export-import ratio and time input only. Performance comparison among different models is made based on absolute and percentage mean square error. Electricity consumption of Turkey is predicted until 2027 using data from 1975 to 2006 along with other economic indicators. The results show that electricity consumption can be modeled using Artificial Neural Networks, and the models can be used to predict future electricity consumption.
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In this study, it was aimed to determine artificial neural network models with different architectures using artificial neural network (ANN) methods used in future prediction studies in recent times and forecast the sales quantities of industrial wood in Turkey with the help of models. The sales quantities of logs, mining poles, other industrial wood, pulpwood, fiber-chip wood, and the total of these five wood groups were analyzed separately. The data used in this study was obtained from the General Directorate of Forestry of Turkey and cumulative monthly data covering the period from January 2001 to December 2016 were used. The most suitable ANN models were determined using performance criteria such as mean absolute percentage error (MAPE), root mean square error (RMSE), and determination coefficient (R 2). As a result, the R 2 and MAPE values of the ANN models were found to be above 99% and below 6%, respectively. The ANNs can be used as a good tool in industrial wood sales forecasts.
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—Electricity is one of the most important needs of human life. In order to provide this need sufficiently, demand for the electricity needs to be predicted in advance. Conducting production oriented studies based on the estimation results is a must. In this study, electricity consumption data of Turkey between the years 1970 and 2014 were collected from Turkish Statistical Institute. Using these data, future electricity consumption of Turkey was predicted with the help of artificial neural network method. Results showed that artificial neural network can be used to predict electricity consumption.
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This study deals with the modeling of the energy consumption in Turkey in order to forecast future projections based on socio-economic and demographic variables (gross domestic product-GDP, population, import and export amounts, and employment) using artificial neural network (ANN) and regression analyses. For this purpose, four diverse models including different indicators were used in the analyses. As the result of the analyses, this research proposes Model 2 as a suitable ANN model (having four independent variables being GDP, population, the amount of import and export) to efficiently estimate the energy consumption for Turkey. The proposed model predicted the energy consumption better than the regression models and the other three ANN models. Thus, the future energy consumption of Turkey is calculated by means of this model under different scenarios. The predicted forecast results by ANN were compared with the official forecasts. Finally, it was concluded that all the scenarios that were analyzed gave lower estimates of the energy consumption than the MENR projections and these scenarios also showed that the future energy consumption of Turkey would vary between 117.0 and 175.4 Mtoe in 2014.
Prediction of net energy consumption based on economic indicators (GNP and GDP) in Turkey
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The most important theme in this study is to obtain equations based on economic indicators (gross national product-GNP and gross domestic product-GDP) and population increase to predict the net energy consumption of Turkey using artificial neural networks (ANNs) in order to determine future level of the energy consumption and make correct investments in Turkey. In this study, three different models were used in order to train the ANN. In one of them (Model 1), energy indicators such as installed capacity, generation, energy import and energy export, in second (Model 2), GNP was used and in the third (Model 3), GDP was used as the input layer of the network. The net energy consumption (NEC) is in the output layer for all models. In order to train the neural network, economic and energy data for last 37 years are used in network for all models. The aim of used different models is to demonstrate the effect of economic indicators on the estimation of NEC. The maximum mean absolute percentage error (MAPE) was found to be 2.322732, 1.110525 and 1.122048 for Models 1, 2 and 3, respectively. R 2 values were obtained as 0.999444, 0.999903 and 0.999903 for training data of Models 1, 2 and 3, respectively. The ANN approach shows greater accuracy for evaluating NEC based on economic indicators. Based on the outputs of the study, the ANN model can be used to estimate the NEC from the country's population and economic indicators with high confidence for planing future projections. r
Forecast of electricity consumption based on an artificial neural network
PROCEEDINGS OF THE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL MANUFACTURING AND METALLURGY (ICIMM 2021), 2022
The study of the possibility of using an artificial neural network (ANN) to predict the hourly electricity consumption by the region of the Russian Federation was carried out. The data on electricity consumption and average daily temperature were selected as the initial data. The initial data were normalized so that they did not exceed the value of one. The number of training examples was 360 and each example had 3 inputs (the training dataset contained 1080 values). At the output, ANN calculated the hourly electricity consumption. A two-layer ANN was used for modeling. ANN was trained using the back propagation method and the conjugate gradient method. ANN training was carried out with an acceptable error of 0.00001 and was within 200 iterations of the conjugate gradient method. The use of an artificial neural network made it possible to satisfactorily describe the actual hourly electricity consumption. The average error was within 4%. The largest deviations of the actual and calculated values of hourly electricity consumption were observed around the 23rd, the 24th every day and amounted to 10-12%. The error increased as the forecast range increased. Calculations were carried out for a different number of neurons in the hidden layer: 7, 10, 13. These changes practically did not affect the forecast results. To increase the accuracy of the hourly forecast, it seems that the hourly temperature change during the day should be taken into account.