获得大数据技术地平线奖：通过识别周期性波动快速可靠地预测电网流量
Winning the Big Data Technologies Horizon Prize: Fast and reliable forecasting of electricity grid traffic by identification of recurrent fluctuations
摘要：
本文描述了我在赢得欧盟大数据技术地平线奖时使用的方法和方法，该奖项是关于电网流量的数据驱动预测。该方法依赖于确定典型的短期经常性波动，随后通过波动回归法加以完善。还讨论了导致选择或放弃不同方法方面的关键点和战略考虑。这些准则包括对变化条件的适应性、对异常值和缺失数据的可靠性、对噪声的鲁棒性以及执行效率。
英文摘要：
This paper provides a description of the approach and methodology I used in winning the European Union Big Data Technologies Horizon Prize on data-driven prediction of electricity grid traffic. The methodology relies on identifying typical short-term recurrent fluctuations, which is subsequently refined through a regression-of-fluctuations approach. The key points and strategic considerations that led to selecting or discarding different methodological aspects are also discussed. The criteria include adaptability to changing conditions, reliability with outliers and missing data, robustness to noise, and efficiency in implementation.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
