Summary of Investigation Of the Impact Of Economic and Social Factors on Energy Demand Through Natural Language Processing, by Yun Bai et al.
Investigation of the Impact of Economic and Social Factors on Energy Demand through Natural Language Processing
by Yun Bai, Simon Camal, Andrea Michiorri
First submitted to arxiv on: 9 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed study investigates the connection between energy demand and various social aspects, such as news about military conflicts, transportation, and economic variables like GDP, unemployment, and inflation. Using natural language processing on a large news corpus, the research sheds light on this important link across five regions in the UK and Ireland. The analysis considers multiple horizons from 1 to 30 days and finds that certain indices are more relevant for forecasting electricity demand depending on the region. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Energy usage is linked to various factors like economic activity and weather, but now researchers explore how news and social aspects impact energy demand. They used big data and computer algorithms to analyze news articles from five regions in the UK and Ireland. The study shows that certain types of news, like military conflicts or transportation, affect electricity demand. It also found that different regions have different factors influencing energy usage, and using this information can improve forecasting by up to 9%. |
Keywords
» Artificial intelligence » Natural language processing
