Prediction of Renewable Energy Consumption of European Union Using Artificial Neural Networks

Abstract

The increasing demand for renewable energy sources attract attention of both researchers and governments. The countries support renewable energy and technologies developed for the efficient use of renewable energy. For this reason, the assessment and prediction of renewable energy consumption is vital for governments. Furthermore, associations put forward long-term and shortterm targets for countries. Therefore, European Union (EU) members provide support schemes for promoting renewable energy consumption. In this study, renewable energy consumption in EU is predicted using artificial neural networks. The World Development indicators which are renewable electricity output, energy use generated from combustible renewables and waste, electricity production from oil, gas and coal sources, energy use generated from alternative and nuclear energy, electricity production from renewable sources excluding hydroelectric, energy imports, energy use, gross domestic product (GDP) and population are evaluated as independent variables using historical data from 1990 to 2015. The results indicate that artificial neural networks provides convenient results in energy demand forecasting as seen in similar studies of the literature.