Using Bees Algorithm and Artificial Neural Network to Forecast World Carbon Dioxide Emission

Abstract

In this study, an integrated multi-layer perceptron neural network and Bees Algorithm is presented for analyzing world CO2 emissions. For this purpose, the following steps are done: STEP 1: In the first step, the Bees Algorithm is applied in order to determine the world's fossil fuels and primary energy demand equations based on socio-economic indicators. The world's population, gross domestic product, oil trade movement, and natural gas trade movement are used as socio-economic indicators in this study. The following scenarios are designed for forecasting each socio-economic indicator in a future time domain: Scenario I: For each socio-economic indicator, several polynomial trend lines are fitted to the observed data and the best fitted polynomial (highest correlation coefficient (R2) value) for each socio-economic indicator is used for future forecasting. Scenario II: For each socio-economic indicator, several neural networks are trained and the best trained network for each socio-economic indicator is used for future forecasting. STEP 2: In the second step, world CO2 emission is projected based on the oil, natural gas, coal, and primary energy consumption using Bees Algorithm. The related data from 1980 to 2006 are used, partly for installing the models (1980–1999) and partly for testing the models (2000–2006). World CO2 emission is forecasted up to year 2040.