Short-term CO<mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e3172" altimg="si126.svg"><mml:msub><mml:mrow/><mml:mrow><mml:mn>2</mml:mn></mml:mrow></mml:msub></mml:math> emissions forecasting based on decomposition approaches and its impact on electricity market scheduling
Neeraj Dhanraj Bokde, Bo Tranberg, Gorm Bruun Andresen
Abstract
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.