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

Viewed

Total article views: 1,096 (including HTML, PDF, and XML)

HTML	PDF	XML	Total	BibTeX	EndNote
829	200	67	1,096	50	62

HTML: 829
PDF: 200
XML: 67
Total: 1,096
BibTeX: 50
EndNote: 62

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	19	4	0	23

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	19	4	0	23

HTML	PDF	XML	Total	BibTeX	EndNote
352	69	9	430	11	18

HTML	PDF	XML	Total	BibTeX	EndNote
477	131	58	666	39	44

Viewed (geographical distribution)

Total article views: 1,096 (including HTML, PDF, and XML) Thereof 1,070 with geography defined and 26 with unknown origin.

Country	#	Views	%


Total:	0
HTML:	0
PDF:	0
XML:	0


Total:	0
HTML:	0
PDF:	0
XML:	0


Total:	0
HTML:	0
PDF:	0
XML:	0

Latest update: 07 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.