Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers
The world’s highest energy consumer (HC) countries currently constitute around 62% of the world energy consumption. Therefore, it is highly important to model their energy consumption to obtain an estimated profile of future world energy consumption. In this study the HCs’ energy consumptions are mo...
| Main Authors: | , , |
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| Format: | Journal Article |
| Published: |
2016
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| Online Access: | http://hdl.handle.net/20.500.11937/36432 |
| _version_ | 1848754768853336064 |
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| author | Aydin, G Jang, Hyong Doo Topal, Erkan |
| author_facet | Aydin, G Jang, Hyong Doo Topal, Erkan |
| author_sort | Aydin, G |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | The world’s highest energy consumer (HC) countries currently constitute around 62% of the world energy consumption. Therefore, it is highly important to model their energy consumption to obtain an estimated profile of future world energy consumption. In this study the HCs’ energy consumptions are modeled using artificial neural networks (ANNs). The models are developed based on economic and demographic variables, which are gross domestic product, population, import, and export of the countries selected. Performance of the derived models is assessed using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) for the testing data. The contribution rate of each variable to the HCs’ energy consumption are also determined to demonstrate the governing variables on the energy consumption. The results show that the correlation coefficients between the ANN predictions and actual energy consumptions are higher than 90%. This indicates a high reliability of the models for forecasting future energy consumption of the HC. Additionally, MAPE, MAE and RMSE values indicate that the ANN models can give adequate forecasting for the HCs’ energy consumption. Furthermore, contribution rates of input variables on energy consumption also indicate that energy consumption of each country studied is governed by different variables. It is expected that this study will be helpful for developing highly applicable energy policies for the HC countries. Furthermore, the results of this study can also be used for determining future trends in the global energy demand. |
| first_indexed | 2025-11-14T08:45:40Z |
| format | Journal Article |
| id | curtin-20.500.11937-36432 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T08:45:40Z |
| publishDate | 2016 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-364322020-09-07T05:52:56Z Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers Aydin, G Jang, Hyong Doo Topal, Erkan The world’s highest energy consumer (HC) countries currently constitute around 62% of the world energy consumption. Therefore, it is highly important to model their energy consumption to obtain an estimated profile of future world energy consumption. In this study the HCs’ energy consumptions are modeled using artificial neural networks (ANNs). The models are developed based on economic and demographic variables, which are gross domestic product, population, import, and export of the countries selected. Performance of the derived models is assessed using mean absolute percentage error (MAPE), mean absolute error (MAE) and root mean square error (RMSE) for the testing data. The contribution rate of each variable to the HCs’ energy consumption are also determined to demonstrate the governing variables on the energy consumption. The results show that the correlation coefficients between the ANN predictions and actual energy consumptions are higher than 90%. This indicates a high reliability of the models for forecasting future energy consumption of the HC. Additionally, MAPE, MAE and RMSE values indicate that the ANN models can give adequate forecasting for the HCs’ energy consumption. Furthermore, contribution rates of input variables on energy consumption also indicate that energy consumption of each country studied is governed by different variables. It is expected that this study will be helpful for developing highly applicable energy policies for the HC countries. Furthermore, the results of this study can also be used for determining future trends in the global energy demand. 2016 Journal Article http://hdl.handle.net/20.500.11937/36432 10.1080/15567249.2015.1075086 restricted |
| spellingShingle | Aydin, G Jang, Hyong Doo Topal, Erkan Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title | Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title_full | Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title_fullStr | Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title_full_unstemmed | Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title_short | Energy consumption modeling using artificial neural networks: The case of the world’s highest consumers |
| title_sort | energy consumption modeling using artificial neural networks: the case of the world’s highest consumers |
| url | http://hdl.handle.net/20.500.11937/36432 |