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- W3095646616 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 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." @default.
- W3095646616 created "2020-11-09" @default.
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- W3095646616 date "2021-01-01" @default.
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- W3095646616 title "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" @default.
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- W3095646616 doi "https://doi.org/10.1016/j.apenergy.2020.116061" @default.
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