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Title: Urban electric load forecasting with mobile phone location data
Authors: Selvarajoo, Stefan
Schläpfer, Markus
Tan, Rui
Keywords: Engineering::Computer science and engineering
Issue Date: 2019
Source: Selvarajoo, S., Schläpfer, M., & Tan, R. (2018). Urban electric load forecasting with mobile phone location data. Proceedings of the 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT), 18403376-. doi:10.1109/acept.2018.8610757
Abstract: 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.
ISBN: 978-1-5386-8137-4
DOI: 10.1109/ACEPT.2018.8610757
Rights: © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. The published version is available at:
Fulltext Permission: open
Fulltext Availability: With Fulltext
Appears in Collections:SCSE Conference Papers

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