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

• Published in: Energy strategy reviews 2022-09, Vol.43, p.100895-100895, Article 100895
• Main Authors: Chaianong, Aksornchan, Winzer, Christian, Gellrich, Mario
• Format: Article
• Language: English
• Subjects: COVID-19; Load forecasting; Machine learning; Random forest; Traffic
• ISSN: 2211-467X; 2211-467X
• DOI: 10.1016/j.esr.2022.100895

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 smaller than the impact of time variables. However, traffic data could improve load forecasting where information on historical load is not available. Another benefit of using traffic data is that it might explain the phenomenon of interest better than historical electricity demand. Some of our findings vary greatly between the two datasets, indicating the importance of studies based on larger numbers of datasets, features, and forecasting approaches. •Traffic data could improve load prediction accuracy before and during COVID-19.•Impact of traffic data on load forecasting is much smaller than time variables.•Traffic data could better improve prediction where load is not available.•Using traffic data could provide insights into the phenomenon of interest.