- Title
- Deploying artificial neural networks for modeling energy demand: international evidence
- Creator
- Bannor B., Emmanuel; Acheampong, Alex O.
- Relation
- International Journal of Energy Sector Management Vol. 14, Issue 2, p. 285-315
- Publisher Link
- http://dx.doi.org/10.1108/IJESM-06-2019-0008
- Publisher
- Emerald Publishing Limited
- Resource Type
- journal article
- Date
- 2019
- Description
- Purpose: This paper aims to use artificial neural networks to develop models for forecasting energy demand for Australia, China, France, India and the USA. Design/methodology/approach: The study used quarterly data that span over the period of 1980Q1-2015Q4 to develop and validate the models. Eight input parameters were used for modeling the demand for energy. Hyperparameter optimization was performed to determine the ideal parameters for configuring each country’s model. To ensure stable forecasts, a repeated evaluation approach was used. After several iterations, the optimal models for each country were selected based on predefined criteria. A multi-layer perceptron with a back-propagation algorithm was used for building each model. Findings: The results suggest that the validated models have developed high generalizing capabilities with insignificant forecasting deviations. The model for Australia, China, France, India and the USA attained high coefficients of determination of 0.981, 0.9837, 0.9425, 0.9137 and 0.9756, respectively. The results from the partial rank correlation coefficient further reveal that economic growth has the highest sensitivity weight on energy demand in Australia, France and the USA while industrialization has the highest sensitivity weight on energy demand in China. Trade openness has the highest sensitivity weight on energy demand in India. Originality/value: This study incorporates other variables such as financial development, foreign direct investment, trade openness, industrialization and urbanization, which are found to have an important effect on energy demand in the model to prevent underestimation of the actual energy demand. Sensitivity analysis is conducted to determine the most influential variables. The study further deploys the models for hands-on predictions of energy demand.
- Subject
- energy production; energy sector; time series analysis; neural networks; energy demand; forecasting; SDG 8; SDG 17; Sustainable Development Goals
- Identifier
- http://hdl.handle.net/1959.13/1474195
- Identifier
- uon:49233
- Identifier
- ISSN:1750-6220
- Language
- eng
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