Urban electric load forecasting with mobile phone location data

In recent years, electrical load forecasting has received continuous research efforts aiming to improve the short-term prediction accuracy of local energy demands. However, current methods are not able to take explicitly into account the dynamic spatial population distribution over the course of a day, which is particularly relevant in dense urban areas. In this paper, we harness society-wide mobile phone data to map the time-varying population distribution in the Trentino region, Italy, and to use these insights for a novel electrical load forecasting method. Our results demonstrate that the integration of aggregated mobile phone data yields compelling forecast models.National Research Foundation (NRF)Accepted versionM.S. acknowledges the Future Cities Laboratory at the Singapore-ETH Centre, which was established collaboratively between ETH Zurich and Singapore’s National Research Foundation (FI 370074016) under its Campus for Research Excellence and Technological Enterprise Programme