Investigation of the Impact of Economic and Social Factors on Energy Demand through Natural Language Processing
<|reference_start|>Investigation of the Impact of Economic and Social Factors on Energy Demand through Natural Language Processing: 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%.<|reference_end|>
@article{bai2024investigation,
    title={Investigation of the Impact of Economic and Social Factors on Energy
  Demand through Natural Language Processing},
    author={Yun Bai, Simon Camal, Andrea Michiorri},
    journal={arXiv preprint arXiv:2406.06641},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06641},
    primaryClass={cs.CL cs.LG}
}