An Interpretable Forecasting Framework for Energy Consumption and Co2 Emissions

Abstract

It is a well-established fact that energy consumption and production, as the primary sources of greenhouse gases, contribute to climate change and global warming issues. The analysis and estimation of the factors that contribute to these harmful gases will be of great assistance in the development of policies to reduce carbon dioxide emissions. In addition to identifying the factors related to energy consumption and CO2 emissions, forecasting the variable of interest as accurately as possible has a key role in increasing the efficiency of energy strategies to be implemented. Unlike studies in the literature, this study not only forecasts the future value of energy consumption and CO2 emissions but also determines the relationship between the predictions and the influential variables by revealing the contribution of each variable to the prediction. For this purpose, the study proposes an interpretable forecasting framework based on values of the Shapley additive explanation (SHAP) to provide a simpler explanation of machine learning (ML) models in forecasting energy consumption and CO2 emissions. The results obtained show that the total electricity generation from different energy sources is found to be the most important variable interacting positively with both energy consumption and CO2 emissions. Also, the influence of the predictors on projections made before and after COVID-19 has changed dramatically. The proposed method may assist policymakers in making future energy investments and establishing energy laws more accurately and efficiently as it explains the drivers of the forecasts.