# News and Load: Social and Economic Drivers of Regional Multi-horizon Electricity Demand Forecasting

arXiv: https://arxiv.org/abs/2406.06641 · HTML: https://mlsanity.com/paper/2406.06641/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 10 of its 46 references that are in the map.
- First posted on arXiv 9 Jun 2024.

## Builds on (most cited first, 10 of 10)

- [A Unified Approach to Interpreting Model Predictions](https://mlsanity.com/paper/1705.07874/) (2017)
- [Optuna: A Next-generation Hyperparameter Optimization Framework](https://mlsanity.com/paper/1907.10902/) (2019)
- [Adaptive Methods for Short-Term Electricity Load Forecasting During COVID-19 Lockdown in France](https://mlsanity.com/paper/2009.06527/) (2020)
- [Probabilistic Gradient Boosting Machines for Large-Scale Probabilistic Regression](https://mlsanity.com/paper/2106.01682/) (2021)
- [A Cross-Domain Approach to Analyzing the Short-Run Impact of COVID-19 on the U.S. Electricity Sector](https://mlsanity.com/paper/2005.06631/) (2020)
- [News and Load: A Quantitative Exploration of Natural Language Processing Applications for Forecasting Day-ahead Electricity System Demand](https://mlsanity.com/paper/2301.07535/) (2023)
- [Using Google Trends as a proxy for occupant behavior to predict building energy consumption](https://mlsanity.com/paper/2111.00426/) (2021)
- [Forecasting Crude Oil Price Using Event Extraction](https://mlsanity.com/paper/2111.09111/) (2021)
- [Crowdsourcing Predictors of Residential Electric Energy Usage](https://mlsanity.com/paper/1709.02739/) (2017)
- [Electricity Demand Forecasting through Natural Language Processing with Long Short-Term Memory Networks](https://mlsanity.com/paper/2309.06793/) (2023)

## Abstract (arXiv)

The relationship between electricity demand and variables such as economic activity and weather patterns is well established. However, this paper explores the connection between electricity demand and social aspects. It further embeds dynamic information about the state of society into energy demand modelling and forecasting approaches. Through the use of natural language processing on a large news corpus, we highlight this important link. This study is conducted in five regions of the UK and Ireland and considers multiple time horizons from 1 to 30 days. It also considers economic variables such as GDP, unemployment and inflation. The textual features used in this study represent central constructs from the word frequencies, topics, word embeddings extracted from the news. The findings indicate that: 1) the textual features are related to various contents, such as military conflicts, transportation, the global pandemic, regional economics, and the international energy market. They exhibit causal relationships with regional electricity demand, which are validated using Granger causality and Double Machine Learning methods. 2) Economic indicators play a more important role in the East Midlands and Northern Ireland, while social indicators are more influential in the West Midlands and the South West of England. 3) The use of these factors improves deterministic forecasting by around 6%.