Modular deep learning approach for wind farm power forecasting and wake loss prediction

Stijn Ally, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, and Jan Helsen

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Total article views: 577 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
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HTML: 411
PDF: 117
XML: 49
Total: 577
BibTeX: 34
EndNote: 31

Views and downloads (calculated since 05 Sep 2024)

Month	HTML	PDF	XML	Total
Sep 2024	127	32	41	200
Oct 2024	40	12	2	54
Nov 2024	93	16	3	112
Dec 2024	60	25	2	87
Jan 2025	23	10	0	33
Feb 2025	18	4	1	23
Mar 2025	31	10	0	41
Apr 2025	19	8	0	27

Cumulative views and downloads (calculated since 05 Sep 2024)

Month	HTML	PDF	XML	Total
Sep 2024	127	32	41	200
Oct 2024	40	12	2	54
Nov 2024	93	16	3	112
Dec 2024	60	25	2	87
Jan 2025	23	10	0	33
Feb 2025	18	4	1	23
Mar 2025	31	10	0	41
Apr 2025	19	8	0	27

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Total article views: 577 (including HTML, PDF, and XML) Thereof 558 with geography defined and 19 with unknown origin.

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Total:	0
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Latest update: 28 Apr 2025

Short summary

Wind farms are crucial for a sustainable energy future. However, their power can fluctuate significantly due to changing weather conditions, which complexly affect their power generation. This paper presents a novel machine-learning-based method to enhance wind farm power predictions, enabling improved power scheduling, trading and grid balancing. This makes wind power more valuable and easier to integrate into the energy system.