Journal
APPLIED ENERGY
Volume 281, Issue -, Pages -Publisher
ELSEVIER SCI LTD
DOI: 10.1016/j.apenergy.2020.116061
Keywords
Forecasting; CO2 emission; Demand flexibility
Categories
Funding
- Apple Inc.
- Aarhus University
- ELFORSK [351-054]
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The energy industry accounts for 46% of all CO2 emissions globally, presenting a significant potential for reduction. A new forecasting method shows a mean absolute percentage error 25% lower than the best performing state-of-the-art model. Scheduling flexible electricity consumption can lead to reductions in CO2 emissions by an average of 25% in France, 17% in Germany, 69% in Norway, 20% in Denmark, and just 3% in Poland.
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 h of electricity consumption in a 24 h 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.
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