  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 CO2 emissions globally, which shows a large potential
for reduction. This paper proposes a novel short-term CO2 emissions forecast to
enable intelligent scheduling of flexible electricity consumption to minimize
the resulting CO2 emissions. Two proposed time series decomposition methods are
developed for short-term forecasting of the CO2 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 hours of electricity consumption in a 24 hour
interval can on average reduce the resulting CO2 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.
