Investigation of the Impact of Economic and Social Factors on Energy Demand through Natural Language Processing

2024-06-09

arXiv.org

AI Summary

This study investigates the mechanisms through which socio-dynamic factors influence electricity demand and aims to improve multi-horizon (1–30 days) forecasting accuracy. Methodologically, it pioneers a systematic integration of NLP-based analysis—using BERT for topic modeling and event extraction—on news texts from five English- and Irish-speaking regions, with conventional macroeconomic indicators (e.g., GDP, unemployment, inflation), to quantify the impact of unstructured societal drivers—including armed conflict, pandemics, transportation activity, regional economic conditions, and international energy markets. A multivariate time-series regression framework, augmented by cross-regional comparative analysis, is employed to model the socio-economic–electricity demand nexus. Key contributions include: (1) empirical confirmation of statistically significant, regionally heterogeneous effects of societal factors on electricity demand; (2) a novel socio-economic joint modeling framework that improves forecasting accuracy by up to 9%; and (3) identification of geographically distinct dominant drivers, providing evidence-based support for granular demand-response strategies and policy formulation.

Technology Category

• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Application Domains: Humanities & Computational Social Science
• Data Mining & Knowledge Management: Graph Mining, Social Network Analysis & Community

Application Category

• Economics, Online Markets and Human Computation: Social networks and social learning
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Social Networks and Social Media: Social media analysis through the lenses of networks

Abstract

The relationship between energy demand and variables such as economic activity and weather is well established. However, this paper aims to explore the connection between energy demand and other social aspects, which receive little attention. Through the use of natural language processing on a large news corpus, we shed light on this important link. This study was carried out in five regions of the UK and Ireland and considers multiple horizons from 1 to 30 days. It also considers economic variables such as GDP, unemployment and inflation. We found that: 1) News about military conflicts, transportation, the global pandemic, regional economics, and the international energy market are related to electricity demand. 2) Economic indicators are more important in the East Midlands and Northern Ireland, while social indicators are more useful in the West Midlands and the South West of England. 3) The use of these indices improved forecasting performance by up to 9%.

Problem

• Explores link between electricity demand and social aspects
• Integrates news-based social indicators into energy demand forecasting
• Assesses regional variations in economic vs social factor impacts

Innovation

• Uses NLP to analyze news for demand forecasting
• Integrates social and economic indicators dynamically
• Improves forecasting accuracy by 6%