Publication
Title
Unveiling the backbone of the renewable energy forecasting process : exploring direct and indirect methods and their applications
Author
Abstract
A myriad of techniques regarding renewable energy forecasting have been proposed in recent literature, commonly classified as physical, statistical, machine learning based or a hybrid form thereof. The renewable energy forecasting process is however elaborate and consists of multiple stages, where different approaches from these four categories apply variably, complicating a holistic classification of the process. This paper resolves this by utilizing the fundamental difference between direct and indirect forecasting in terms of model complexity, data availability, spatial and time horizons as the backbone to structure this intricate forecasting process. As such, a significant step towards a generalized framework for renewable energy forecasting is presented. Additionally, a most promising recommendation emerges: leveraging physics-based knowledge from indirect models to enhance training of direct methods.
Language
English
Source (journal)
Energy Reports
Publication
Amsterdam : Elsevier , 2024
ISSN
23524847
2352-4847
DOI
10.1016/J.EGYR.2023.12.031
Volume/pages
11 (2024) , p. 544-557
ISI
001136344400001
Full text (Publisher's DOI)
Full text (open access)
UAntwerpen
Faculty/Department
Research group
Project info
Extensible Tools for Renewable ENergy Decision making (E-TREND).
Publication type
Subject
Affiliation
Publications with a UAntwerp address
External links
Web of Science
Record
Identifier
Creation 01.02.2024
Last edited 08.08.2024
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