News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand

TL;DR

This paper demonstrates that incorporating social event-related textual features from natural language processing can enhance day-ahead electricity demand forecasts, offering new insights into demand drivers beyond traditional weather and social factors.

Contribution

It introduces a novel approach combining NLP techniques with demand forecasting to leverage social event information from unstructured text for improved electricity demand predictions.

Findings

Textual features improve forecast accuracy

Social events influence electricity demand patterns

Feasibility of using unstructured text in demand forecasting

Abstract

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.