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

The relationship between electricity demand and weather has been established for a long time and is one of the cornerstones in load prediction for operation and planning, along with behavioral and social aspects such as calendars or significant events. This paper explores how and why the social information contained in the news can be used better to understand aggregate population behaviour in terms of energy demand. The work is done through experiments analysing the impact of predicting features extracted from national news on day-ahead electric demand prediction. The results are compared to a benchmark model trained exclusively on the calendar and meteorological information. Experimental results showed that the best-performing model reduced the official standard errors around 4%, 11%, and 10% in terms of RMSE, MAE, and SMAPE. The best-performing methods are: word frequency identified COVID-19-related keywords; topic distribution that identified news on the pandemic and internal politics; global word embeddings that identified news about international conflicts. This study brings a new perspective to traditional electricity demand analysis and confirms the feasibility of improving its predictions with unstructured information contained in texts, with potential consequences in sociology and economics.