Accurate CO2 forecasting plays an important role in energy planning. However, in the annual forecasting studies on CO2 emissions, the seasonal effects cannot be predicted. To overcome this problem, this study proposed a novel prediction model based on the seasonally optimised fractional...

The goal of this study is to use machine-learning (ML) techniques and empirical big data to examine the influence of the COVID-19 pandemic on electricity usage and electricity demand forecasting accuracy in buildings in Qatar over time and across sectors. Furthermore, this study statistically...

Nowadays, many countries adopt an active agenda to mitigate the impact of greenhouse gas emissions by moving towards less polluting energy generation technologies. The environmental costs, directly or indirectly generated to achieve such a challenging objective, remain however largely...