The relationship between electricity demand and weather is well established in power systems, along with the importance of behavioral and social aspects such as holidays and significant events. This study explores the link between electricity demand and more nuanced information about social events. This is done using mature Natural Language Processing (NLP) and demand forecasting techniques. The results indicate that day-ahead forecasts are improved by textual features such as word frequencies, public sentiments, topic distributions, and word embeddings. The social events contained in these features include global pandemics, politics, international conflicts, transportation, etc. Causality effects and correlations are discussed to propose explanations for the mechanisms behind the links highlighted. This study is believed to bring a new perspective to traditional electricity demand analysis. It confirms the feasibility of improving forecasts from unstructured text, with potential consequences for sociology and economics.

翻译：电力需求与天气之间的关系在电力系统中已得到充分验证，同时节假日和重大事件等行为与社会因素的重要性也已明确。本研究通过成熟的自然语言处理（NLP）和需求预测技术，探索电力需求与社会事件中更细微信息之间的关联。结果表明，通过词频、公众情感、主题分布和词嵌入等文本特征可提升日前预测的准确性。这些特征涵盖的社会事件包括全球疫情、政治动态、国际冲突、交通运输等。本文通过讨论因果效应与相关性，提出对所揭示关联背后机制的解释。该研究被认为为传统电力需求分析提供了新视角，证实了从非结构化文本改进预测的可行性，对社会学和经济学领域具有潜在影响。