Forecasting net energy consumption using 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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Energy demand is increasing day by day in parallel with economic growth, especially for the rapidly developing countries. In order to achieve a sustainable economic growth, long-term targets are being put to manage the operation of market in a good way. Turkey is an emerging and rapidly developing country so its energy demand has increased rapidly to meet the growing economy. Therefore, forecasting Turkey's energy demand accurately is of great importance to achieve a sustainable economic growth. The main goal of this study is to develop the equation for forecasting energy demand using the backpropagation algorithm which is one of the artificial neural-network models to determine the future level of energy demand. This study presents the predictions for the years 2017-2020. The results of the energy demand estimations found in this study are compared with the official estimations of the MENR. It is concluded that official estimations for Turkey's energy demand are dramatically higher than forecasting value presented in this study.
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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.
Energy Conversion and Management, 2008
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Forecasting Turkey’s Energy Demand Using Artificial Neural Networks: Three Scenario Applications
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Energy Consumption Forecasting Using Seasonal ARIMA with Artificial Neural Networks Models
International Journal of Business and Management, 2016
In many areas such as financial, energy, economics, the historical data are non-stationary and contain trend and seasonal variations. The goal is to forecast the energy consumption in U.S. using two approaches, namely the statistical approach (SARIMA) and Neural Networks approach (ANN), and compare them in order to find the best model for forecasting. The energy area has an important role in the development of countries, thus, consumption planning of energy must be made accurately, despite they are governed by other factors such that population, gross domestic product (GDP), weather vagaries, storage capacity etc. This paper examines the forecasting performance for the residential energy consumption data of United States between SARIMA and ANN methodologies. The multi-layer perceptron (MLP) architecture is used in the artificial neural networks methodology. According to the obtained results, we conclude that the neural network model has slight superiority over SARIMA model and those models are not directional.