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 d...
Saved in:
| Main Authors: | , , |
|---|---|
| Other Authors: | |
| Format: | Conference or Workshop Item |
| Language: | English |
| Published: |
2020
|
| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/145170 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| Institution: | Nanyang Technological University |
| Language: | English |
| Summary: | 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. |
|---|