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The relationship between electricity demand and weather has been establishedfor a long time and is one of the cornerstones in load prediction for operationand planning, along with behavioral and social aspects such as calendars orsignificant events. This paper explores how and why the social informationcontained in the news can be used better to understand aggregate populationbehaviour in terms of energy demand. The work is done through experimentsanalysing the impact of predicting features extracted from national news onday-ahead electric demand prediction. The results are compared to a benchmarkmodel trained exclusively on the calendar and meteorological information.Experimental results showed that the best-performing model reduced the officialstandard errors around 4%, 11%, and 10% in terms of RMSE, MAE, and SMAPE. Thebest-performing methods are: word frequency identified COVID-19-relatedkeywords; topic distribution that identified news on the pandemic and internalpolitics; global word embeddings that identified news about internationalconflicts. This study brings a new perspective to traditional electricitydemand analysis and confirms the feasibility of improving its predictions withunstructured information contained in texts, with potential consequences insociology and economics.

A Quantitative Exploration of Natural Language Processing Applications for Electricity Demand Analysis