论文标题
基于分解方法及其对电力市场计划的影响，短期二氧化碳排放预测
Short-term CO2 emissions forecasting based on decomposition approaches and its impact on electricity market scheduling
论文作者
论文摘要
世界面临着与全球变暖和温室气体排放相关的重大挑战，这是主要导致因素。 2017年，能源行业占全球所有二氧化碳排放量的46％，这显示出较大的减少潜力。本文提出了一种新颖的短期二氧化碳排放预测，以实现智能的柔性电力消耗计划，以最大程度地减少所得的二氧化碳排放。开发了两种提出的时间序列分解方法，用于短期预测电力的二氧化碳排放。这些反过来又是针对一系列最新模型的板凳标记。结果是一种新的预测方法，其面向日期电力市场的48小时地平线。对法国的预测基准表明，新方法具有平均绝对百分比误差，比最佳性能最先进的模型低25％。此外，研究了五个欧洲国家 /地区的预测用于安排灵活的电力消耗。在24小时内，安排4小时的4小时电力量可以平均将所得的二氧化碳排放量减少25％，德国的17％，挪威的69％，丹麦的20％，而波兰仅在当天随机的间隔时在波兰只有3％。
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.
