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

These authors contributed equally to this work. 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. Electricity demand modelling is a fundamental process in power system planning, operation, and energy trading [1]. In order to avoid additional carbon emissions from excess electricity generation and the high costs of electricity storage, electricity demand and supply should be matched over time [2]. Demand forecasting has become a means of enabling power dispatch, planning generation schedules, and integrating renewable energy sources [3]. Electricity demand forecasting is linked to various factors, including weather, economic activity, and major events.