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...
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| Main Authors: | , , |
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| 其他作者: | |
| 格式: | Conference or Workshop Item |
| 語言: | English |
| 出版: |
2020
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| 主題: | |
| 在線閱讀: | https://hdl.handle.net/10356/145170 |
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| 機構: | Nanyang Technological University |
| 語言: | English |
| 總結: | 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. |
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