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:	Selvarajoo, Stefan, Schläpfer, Markus, Tan, Rui
Other Authors:	School of Computer Science and Engineering
Format:	Conference or Workshop Item
Language:	English
Published:	2020
Subjects:	Engineering::Computer science and engineering Data Analytics Electrical Load Forecasting
Online Access:	https://hdl.handle.net/10356/145170
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Institution:	Nanyang Technological University
Language:	English

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spelling	sg-ntu-dr.10356-1451702020-12-14T09:05:57Z Urban electric load forecasting with mobile phone location data Selvarajoo, Stefan Schläpfer, Markus Tan, Rui School of Computer Science and Engineering 2018 Asian Conference on Energy, Power and Transportation Electrification (ACEPT) Engineering::Computer science and engineering Data Analytics Electrical Load Forecasting 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 version M.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. 2020-12-14T09:05:57Z 2020-12-14T09:05:57Z 2019 Conference Paper 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 978-1-5386-8137-4 https://hdl.handle.net/10356/145170 10.1109/ACEPT.2018.8610757 en © 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: https://doi.org/10.1109/ACEPT.2018.8610757 application/pdf
institution	Nanyang Technological University
building	NTU Library
continent	Asia
country	Singapore Singapore
content_provider	NTU Library
collection	DR-NTU
language	English
topic	Engineering::Computer science and engineering Data Analytics Electrical Load Forecasting
spellingShingle	Engineering::Computer science and engineering Data Analytics Electrical Load Forecasting Selvarajoo, Stefan Schläpfer, Markus Tan, Rui Urban electric load forecasting with mobile phone location data
description	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.
author2	School of Computer Science and Engineering
author_facet	School of Computer Science and Engineering Selvarajoo, Stefan Schläpfer, Markus Tan, Rui
format	Conference or Workshop Item
author	Selvarajoo, Stefan Schläpfer, Markus Tan, Rui
author_sort	Selvarajoo, Stefan
title	Urban electric load forecasting with mobile phone location data
title_short	Urban electric load forecasting with mobile phone location data
title_full	Urban electric load forecasting with mobile phone location data
title_fullStr	Urban electric load forecasting with mobile phone location data
title_full_unstemmed	Urban electric load forecasting with mobile phone location data
title_sort	urban electric load forecasting with mobile phone location data
publishDate	2020
url	https://hdl.handle.net/10356/145170
_version_	1688665484218597376

Urban electric load forecasting with mobile phone location data

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