新闻与负荷：区域多时间尺度电力需求预测的社会与经济驱动因素
News and Load: Social and Economic Drivers of Regional Multi-horizon Electricity Demand Forecasting
AI总结：
本文利用新闻文本特征（词频、主题、词嵌入）结合经济指标，对英国和爱尔兰五个区域1-30天电力需求进行预测，发现社会与经济因素均具因果影响，可提升预测精度约6%。
摘要：
电力需求与经济活动和天气模式等变量之间的关系已得到充分证实。然而，本文探讨了电力需求与社会方面之间的联系，并进一步将关于社会状态的动态信息嵌入到能源需求建模和预测方法中。通过对大型新闻语料库应用自然语言处理，我们突出了这一重要联系。本研究在英国的五个地区和爱尔兰进行，考虑了从1天到30天的多个时间范围，同时考虑了GDP、失业率和通货膨胀等经济变量。本研究中使用的文本特征代表了从新闻中提取的词频、主题和词嵌入中的核心构念。研究结果表明：1）文本特征与各种内容相关，如军事冲突、交通、全球大流行、区域经济和国际能源市场。它们与区域电力需求表现出因果关系，并通过格兰杰因果性和双重机器学习方法进行了验证。2）经济指标在东米德兰兹和北爱尔兰发挥更重要的作用，而社会指标在西米德兰兹和英格兰西南部更具影响力。3）使用这些因素可将确定性预测提高约6%。
英文摘要：
The relationship between electricity demand and variables such as economic activity and weather patterns is well established. However, this paper explores the connection between electricity demand and social aspects. It further embeds dynamic information about the state of society into energy demand modelling and forecasting approaches. Through the use of natural language processing on a large news corpus, we highlight this important link. This study is conducted in five regions of the UK and Ireland and considers multiple time horizons from 1 to 30 days. It also considers economic variables such as GDP, unemployment and inflation. The textual features used in this study represent central constructs from the word frequencies, topics, word embeddings extracted from the news. The findings indicate that: 1) the textual features are related to various contents, such as military conflicts, transportation, the global pandemic, regional economics, and the international energy market. They exhibit causal relationships with regional electricity demand, which are validated using Granger causality and Double Machine Learning methods. 2) Economic indicators play a more important role in the East Midlands and Northern Ireland, while social indicators are more influential in the West Midlands and the South West of England. 3) The use of these factors improves deterministic forecasting by around 6%.
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。
