  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.
