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Article: Seamless and multi-resolution energy forecasting

TitleSeamless and multi-resolution energy forecasting
Authors
KeywordsEnergy Forecasting
Laplace Transform
Machine Learning
Multi-resolution System Operation
Issue Date21-Aug-2024
PublisherInstitute of Electrical and Electronics Engineers
Citation
IEEE Transactions on Smart Grid, 2024 How to Cite?
Abstract

Forecasting is pivotal in energy systems, by providing fundamentals for operation at different horizons and resolutions. Though energy forecasting has been widely studied for capturing temporal information, very few works concentrate on the frequency information provided by forecasts. They are consequently often limited to single-resolution applications (e.g., hourly). Here, we propose a unified energy forecasting framework based on Laplace transform in the multi-resolution context. The forecasts can be seamlessly produced at different desired resolutions without re-training or post-processing. Case studies on both energy demand and supply data show that the forecasts from our proposed method can provide accurate information in both time and frequency domains. Across the resolutions, the forecasts also demonstrate high consistency. More importantly, we explore the operational effects of our produced forecasts in the day-ahead and intra-day energy scheduling. The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed. Significant operational benefits are obtained.


Persistent Identifierhttp://hdl.handle.net/10722/350128
ISSN
2023 Impact Factor: 8.6
2023 SCImago Journal Rankings: 4.863

 

DC FieldValueLanguage
dc.contributor.authorWang, Chenxi-
dc.contributor.authorPinson, Pierre-
dc.contributor.authorWang, Yi-
dc.date.accessioned2024-10-21T03:56:20Z-
dc.date.available2024-10-21T03:56:20Z-
dc.date.issued2024-08-21-
dc.identifier.citationIEEE Transactions on Smart Grid, 2024-
dc.identifier.issn1949-3053-
dc.identifier.urihttp://hdl.handle.net/10722/350128-
dc.description.abstract<p>Forecasting is pivotal in energy systems, by providing fundamentals for operation at different horizons and resolutions. Though energy forecasting has been widely studied for capturing temporal information, very few works concentrate on the frequency information provided by forecasts. They are consequently often limited to single-resolution applications (e.g., hourly). Here, we propose a unified energy forecasting framework based on Laplace transform in the multi-resolution context. The forecasts can be seamlessly produced at different desired resolutions without re-training or post-processing. Case studies on both energy demand and supply data show that the forecasts from our proposed method can provide accurate information in both time and frequency domains. Across the resolutions, the forecasts also demonstrate high consistency. More importantly, we explore the operational effects of our produced forecasts in the day-ahead and intra-day energy scheduling. The relationship between (i) errors in both time and frequency domains and (ii) operational value of the forecasts is analysed. Significant operational benefits are obtained.<br></p>-
dc.languageeng-
dc.publisherInstitute of Electrical and Electronics Engineers-
dc.relation.ispartofIEEE Transactions on Smart Grid-
dc.subjectEnergy Forecasting-
dc.subjectLaplace Transform-
dc.subjectMachine Learning-
dc.subjectMulti-resolution System Operation-
dc.titleSeamless and multi-resolution energy forecasting -
dc.typeArticle-
dc.identifier.doi10.1109/TSG.2024.3447162-
dc.identifier.scopuseid_2-s2.0-85201788858-
dc.identifier.eissn1949-3061-
dc.identifier.issnl1949-3053-

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