Impacts of traffic data on short-term residential load forecasting before and during the COVID-19 pandemic

ABSTRACT: Accurate load forecasting is essential for power-sector planning and management. This applies during normal situations as well as phase changes such as the Coronavirus (COVID-19) pandemic due to variations in electricity consumption that made it difficult for system operators to forecast load accurately. So far, few studies have used traffic data to improve load prediction accuracy. This paper aims to investigate the influence of traffic data in combination with other commonly used features (historical load, weather, and time) – to better predict short-term residential electricity consumption. Based on data from two selected distribution grid areas in Switzerland and random forest as a forecasting technique, the findings suggest that the impact of traffic data on load forecasts is much sma

SUBMITTER: Chaianong A 

PROVIDER: S-EPMC9283696 | biostudies-literature | 2022 Jul

REPOSITORIES: biostudies-literature