Short-term CO 2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling

The world is facing major challenges related to global warming and emissions of greenhouse gases is a major causing factor. In 2017, energy industries accounted for 46% of all CO 2 emissions globally, which shows a large potential for reduction. This paper proposes a novel short-term CO 2 emissions forecast to enable intelligent scheduling of flexible electricity consumption to minimize the resulting CO 2 emissions. Two proposed time series decomposition methods are developed for short-term forecasting of the CO 2 emissions of electricity. These are in turn bench-marked against a set of state-of-the-art models. The result is a new forecasting method with a 48-hour horizon targeted the day-ahead electricity market. Forecasting benchmarks for France show that the new method has a mean absolute percentage error that is 25% lower than the best performing state-of-the-art model. Further, application of the forecast for scheduling flexible electricity consumption is studied for five European countries. Scheduling a flexible block of 4 h of electricity consumption in a 24 h interval can on average reduce the resulting CO 2 emissions by 25% in France, 17% in Germany , 69% in Norway, 20% in Denmark, and just 3% in Poland when compared to consuming at random intervals during the day.