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: 1,216 (including HTML, PDF, and XML)

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PDF: 215
XML: 79
Total: 1,216
BibTeX: 53
EndNote: 71

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	114	18	3	135
May 2025	188	44	6	238
Jun 2025	116	25	9	150
Jul 2025	112	19	12	143

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	114	18	3	135
May 2025	188	44	6	238
Jun 2025	116	25	9	150
Jul 2025	112	19	12	143

HTML	PDF	XML	Total	BibTeX	EndNote
428	81	21	530	14	23

HTML	PDF	XML	Total	BibTeX	EndNote
494	134	58	686	39	48

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Total article views: 1,216 (including HTML, PDF, and XML) Thereof 1,192 with geography defined and 24 with unknown origin.

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Latest update: 30 Jul 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.