Machine learning approaches for predictions of CO2 emissions in the building sector

The building sector has historically accounted for around 50% of the energy-related carbon dioxide (CO2) emissions on a global scale. As a result, it attracts significant attention as part of the worldwide effort to decarbonize the energy system. This paper presents and compares a variety of Machine Learning (ML)-based approaches for long-term predictions of CO2 emissions from buildings until the year 2050. These approaches include Linear Regression, ARIMA (Autoregressive Integrated Moving Average), Shallow Neural Networks, and Deep Neural Networks, all conducted using both univariate and multivariate modelling and with different approaches for the Features Extraction process; namely, the lagged values approach and the polynomial transformation. The analysis is conducted for different regions of the world including Brazil, India, China, South Africa, the United States, Great Britain, the World-average and the European Union. A variety of tests are conducted to evaluate and compare the predictive performance of the different ML approaches.