Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data Article Swipe
Simon Daenens
,
Timothy Verstraeten
,
Pieter-Jan Daems
,
Ann Nowé
,
Jan Helsen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/wes-10-1137-2025
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.5194/wes-10-1137-2025
This paper introduces a novel model for predicting wind turbine power output in a wind farm at a high temporal resolution of 30 s. The wind farm is represented as a graph, with graph neural networks (GNNs) used to aggregate selected input features from neighboring turbines. A temporal component is introduced by feeding a time series of input features into the graph, processed through a long short-term memory (LSTM) network before being passed to the GNN. Our model is integrated into a normal behavior model (NBM) framework for analyzing power loss events in wind farms. The results show that both the spatial and the spatio-temporal GNN models outperform traditional data-driven power curve methods, achieving reductions in the mean absolute error (MAE) of approximately 22.6 % and 30.3 %, respectively, and in the mean absolute percentage error (MAPE) of around 20.7 % and 30.5 %, respectively. Notably, the spatio-temporal GNN demonstrates superior performance, attributed to its ability to effectively capture both spatial and temporal dynamics. Additionally, the model achieves remarkable agreement with SCADA-derived energy ratios across the full range of wind directions, with a weighted average error of 0.0373, an improvement of approximately 57.4 % compared to the power curve binning method. This advantage is especially pronounced under waked conditions, where traditional methods such as the power curve and multilayer perceptron (MLP) models exhibit significantly higher error rates. Beyond power prediction, we illustrate the model's effectiveness in detecting and analyzing instances of reduced performance and its ability to identify various types of abnormal events beyond what is recorded in standard status logs. Compared to the power curve method, the spatio-temporal GNN reduces the rate of undetected power loss events from 12.6 % to just 0.02 %, demonstrating a substantial improvement in capturing abnormal events.
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/wes-10-1137-2025
- https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf
- OA Status
- gold
- Cited By
- 2
- References
- 27
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4411658967
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
-
https://openalex.org/W4411658967Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/wes-10-1137-2025Digital Object Identifier
- Title
-
Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA dataWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-06-25Full publication date if available
- Authors
-
Simon Daenens, Timothy Verstraeten, Pieter-Jan Daems, Ann Nowé, Jan HelsenList of authors in order
- Landing page
-
https://doi.org/10.5194/wes-10-1137-2025Publisher landing page
- PDF URL
-
https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdfDirect OA link when available
- Concepts
-
SCADA, Submarine pipeline, Offshore wind power, Graph, Artificial neural network, Marine engineering, Computer science, Wind power, Data mining, Environmental science, Real-time computing, Artificial intelligence, Engineering, Electrical engineering, Geotechnical engineering, Theoretical computer scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2Per-year citation counts (last 5 years)
- References (count)
-
27Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
Full payload
| id | https://openalex.org/W4411658967 |
|---|---|
| doi | https://doi.org/10.5194/wes-10-1137-2025 |
| ids.doi | https://doi.org/10.5194/wes-10-1137-2025 |
| ids.openalex | https://openalex.org/W4411658967 |
| fwci | 4.04313148 |
| type | article |
| title | Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data |
| awards[0].id | https://openalex.org/G6875394642 |
| awards[0].funder_id | https://openalex.org/F4320327336 |
| awards[0].display_name | |
| awards[0].funder_award_id | "Onderzoeksprogramma Artificiële Intelligentie (AI) Vlaanderen” program |
| awards[0].funder_display_name | Vlaamse regering |
| biblio.issue | 6 |
| biblio.volume | 10 |
| biblio.last_page | 1152 |
| biblio.first_page | 1137 |
| topics[0].id | https://openalex.org/T11052 |
| topics[0].field.id | https://openalex.org/fields/22 |
| topics[0].field.display_name | Engineering |
| topics[0].score | 0.9994999766349792 |
| topics[0].domain.id | https://openalex.org/domains/3 |
| topics[0].domain.display_name | Physical Sciences |
| topics[0].subfield.id | https://openalex.org/subfields/2208 |
| topics[0].subfield.display_name | Electrical and Electronic Engineering |
| topics[0].display_name | Energy Load and Power Forecasting |
| topics[1].id | https://openalex.org/T13650 |
| topics[1].field.id | https://openalex.org/fields/17 |
| topics[1].field.display_name | Computer Science |
| topics[1].score | 0.9909999966621399 |
| topics[1].domain.id | https://openalex.org/domains/3 |
| topics[1].domain.display_name | Physical Sciences |
| topics[1].subfield.id | https://openalex.org/subfields/1702 |
| topics[1].subfield.display_name | Artificial Intelligence |
| topics[1].display_name | Computational Physics and Python Applications |
| topics[2].id | https://openalex.org/T11276 |
| topics[2].field.id | https://openalex.org/fields/17 |
| topics[2].field.display_name | Computer Science |
| topics[2].score | 0.9733999967575073 |
| topics[2].domain.id | https://openalex.org/domains/3 |
| topics[2].domain.display_name | Physical Sciences |
| topics[2].subfield.id | https://openalex.org/subfields/1702 |
| topics[2].subfield.display_name | Artificial Intelligence |
| topics[2].display_name | Solar Radiation and Photovoltaics |
| funders[0].id | https://openalex.org/F4320327336 |
| funders[0].ror | |
| funders[0].display_name | Vlaamse regering |
| is_xpac | False |
| apc_list.value | 1050 |
| apc_list.currency | EUR |
| apc_list.value_usd | 1132 |
| apc_paid.value | 1050 |
| apc_paid.currency | EUR |
| apc_paid.value_usd | 1132 |
| concepts[0].id | https://openalex.org/C113863187 |
| concepts[0].level | 2 |
| concepts[0].score | 0.8928895592689514 |
| concepts[0].wikidata | https://www.wikidata.org/wiki/Q17498 |
| concepts[0].display_name | SCADA |
| concepts[1].id | https://openalex.org/C162284963 |
| concepts[1].level | 2 |
| concepts[1].score | 0.6131157279014587 |
| concepts[1].wikidata | https://www.wikidata.org/wiki/Q17106102 |
| concepts[1].display_name | Submarine pipeline |
| concepts[2].id | https://openalex.org/C8735168 |
| concepts[2].level | 3 |
| concepts[2].score | 0.5848485231399536 |
| concepts[2].wikidata | https://www.wikidata.org/wiki/Q61637704 |
| concepts[2].display_name | Offshore wind power |
| concepts[3].id | https://openalex.org/C132525143 |
| concepts[3].level | 2 |
| concepts[3].score | 0.5531972050666809 |
| concepts[3].wikidata | https://www.wikidata.org/wiki/Q141488 |
| concepts[3].display_name | Graph |
| concepts[4].id | https://openalex.org/C50644808 |
| concepts[4].level | 2 |
| concepts[4].score | 0.5464490652084351 |
| concepts[4].wikidata | https://www.wikidata.org/wiki/Q192776 |
| concepts[4].display_name | Artificial neural network |
| concepts[5].id | https://openalex.org/C199104240 |
| concepts[5].level | 1 |
| concepts[5].score | 0.4572228789329529 |
| concepts[5].wikidata | https://www.wikidata.org/wiki/Q118291 |
| concepts[5].display_name | Marine engineering |
| concepts[6].id | https://openalex.org/C41008148 |
| concepts[6].level | 0 |
| concepts[6].score | 0.45609772205352783 |
| concepts[6].wikidata | https://www.wikidata.org/wiki/Q21198 |
| concepts[6].display_name | Computer science |
| concepts[7].id | https://openalex.org/C78600449 |
| concepts[7].level | 2 |
| concepts[7].score | 0.4284725487232208 |
| concepts[7].wikidata | https://www.wikidata.org/wiki/Q43302 |
| concepts[7].display_name | Wind power |
| concepts[8].id | https://openalex.org/C124101348 |
| concepts[8].level | 1 |
| concepts[8].score | 0.41117554903030396 |
| concepts[8].wikidata | https://www.wikidata.org/wiki/Q172491 |
| concepts[8].display_name | Data mining |
| concepts[9].id | https://openalex.org/C39432304 |
| concepts[9].level | 0 |
| concepts[9].score | 0.3978388011455536 |
| concepts[9].wikidata | https://www.wikidata.org/wiki/Q188847 |
| concepts[9].display_name | Environmental science |
| concepts[10].id | https://openalex.org/C79403827 |
| concepts[10].level | 1 |
| concepts[10].score | 0.38852354884147644 |
| concepts[10].wikidata | https://www.wikidata.org/wiki/Q3988 |
| concepts[10].display_name | Real-time computing |
| concepts[11].id | https://openalex.org/C154945302 |
| concepts[11].level | 1 |
| concepts[11].score | 0.3059808015823364 |
| concepts[11].wikidata | https://www.wikidata.org/wiki/Q11660 |
| concepts[11].display_name | Artificial intelligence |
| concepts[12].id | https://openalex.org/C127413603 |
| concepts[12].level | 0 |
| concepts[12].score | 0.25301724672317505 |
| concepts[12].wikidata | https://www.wikidata.org/wiki/Q11023 |
| concepts[12].display_name | Engineering |
| concepts[13].id | https://openalex.org/C119599485 |
| concepts[13].level | 1 |
| concepts[13].score | 0.17337578535079956 |
| concepts[13].wikidata | https://www.wikidata.org/wiki/Q43035 |
| concepts[13].display_name | Electrical engineering |
| concepts[14].id | https://openalex.org/C187320778 |
| concepts[14].level | 1 |
| concepts[14].score | 0.10721799731254578 |
| concepts[14].wikidata | https://www.wikidata.org/wiki/Q1349130 |
| concepts[14].display_name | Geotechnical engineering |
| concepts[15].id | https://openalex.org/C80444323 |
| concepts[15].level | 1 |
| concepts[15].score | 0.08797314763069153 |
| concepts[15].wikidata | https://www.wikidata.org/wiki/Q2878974 |
| concepts[15].display_name | Theoretical computer science |
| keywords[0].id | https://openalex.org/keywords/scada |
| keywords[0].score | 0.8928895592689514 |
| keywords[0].display_name | SCADA |
| keywords[1].id | https://openalex.org/keywords/submarine-pipeline |
| keywords[1].score | 0.6131157279014587 |
| keywords[1].display_name | Submarine pipeline |
| keywords[2].id | https://openalex.org/keywords/offshore-wind-power |
| keywords[2].score | 0.5848485231399536 |
| keywords[2].display_name | Offshore wind power |
| keywords[3].id | https://openalex.org/keywords/graph |
| keywords[3].score | 0.5531972050666809 |
| keywords[3].display_name | Graph |
| keywords[4].id | https://openalex.org/keywords/artificial-neural-network |
| keywords[4].score | 0.5464490652084351 |
| keywords[4].display_name | Artificial neural network |
| keywords[5].id | https://openalex.org/keywords/marine-engineering |
| keywords[5].score | 0.4572228789329529 |
| keywords[5].display_name | Marine engineering |
| keywords[6].id | https://openalex.org/keywords/computer-science |
| keywords[6].score | 0.45609772205352783 |
| keywords[6].display_name | Computer science |
| keywords[7].id | https://openalex.org/keywords/wind-power |
| keywords[7].score | 0.4284725487232208 |
| keywords[7].display_name | Wind power |
| keywords[8].id | https://openalex.org/keywords/data-mining |
| keywords[8].score | 0.41117554903030396 |
| keywords[8].display_name | Data mining |
| keywords[9].id | https://openalex.org/keywords/environmental-science |
| keywords[9].score | 0.3978388011455536 |
| keywords[9].display_name | Environmental science |
| keywords[10].id | https://openalex.org/keywords/real-time-computing |
| keywords[10].score | 0.38852354884147644 |
| keywords[10].display_name | Real-time computing |
| keywords[11].id | https://openalex.org/keywords/artificial-intelligence |
| keywords[11].score | 0.3059808015823364 |
| keywords[11].display_name | Artificial intelligence |
| keywords[12].id | https://openalex.org/keywords/engineering |
| keywords[12].score | 0.25301724672317505 |
| keywords[12].display_name | Engineering |
| keywords[13].id | https://openalex.org/keywords/electrical-engineering |
| keywords[13].score | 0.17337578535079956 |
| keywords[13].display_name | Electrical engineering |
| keywords[14].id | https://openalex.org/keywords/geotechnical-engineering |
| keywords[14].score | 0.10721799731254578 |
| keywords[14].display_name | Geotechnical engineering |
| keywords[15].id | https://openalex.org/keywords/theoretical-computer-science |
| keywords[15].score | 0.08797314763069153 |
| keywords[15].display_name | Theoretical computer science |
| language | en |
| locations[0].id | doi:10.5194/wes-10-1137-2025 |
| locations[0].is_oa | True |
| locations[0].source.id | https://openalex.org/S4210189655 |
| locations[0].source.issn | 2366-7443, 2366-7451 |
| locations[0].source.type | journal |
| locations[0].source.is_oa | True |
| locations[0].source.issn_l | 2366-7443 |
| locations[0].source.is_core | True |
| locations[0].source.is_in_doaj | True |
| locations[0].source.display_name | Wind energy science |
| locations[0].source.host_organization | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_name | Copernicus Publications |
| locations[0].source.host_organization_lineage | https://openalex.org/P4310313756 |
| locations[0].source.host_organization_lineage_names | Copernicus Publications |
| locations[0].license | cc-by |
| locations[0].pdf_url | https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf |
| locations[0].version | publishedVersion |
| locations[0].raw_type | journal-article |
| locations[0].license_id | https://openalex.org/licenses/cc-by |
| locations[0].is_accepted | True |
| locations[0].is_published | True |
| locations[0].raw_source_name | Wind Energy Science |
| locations[0].landing_page_url | https://doi.org/10.5194/wes-10-1137-2025 |
| locations[1].id | pmh:oai:doaj.org/article:80e476938a07408a995fc24056f55ad8 |
| locations[1].is_oa | False |
| locations[1].source.id | https://openalex.org/S4306401280 |
| locations[1].source.issn | |
| locations[1].source.type | repository |
| locations[1].source.is_oa | False |
| locations[1].source.issn_l | |
| locations[1].source.is_core | False |
| locations[1].source.is_in_doaj | False |
| locations[1].source.display_name | DOAJ (DOAJ: Directory of Open Access Journals) |
| locations[1].source.host_organization | |
| locations[1].source.host_organization_name | |
| locations[1].license | |
| locations[1].pdf_url | |
| locations[1].version | submittedVersion |
| locations[1].raw_type | article |
| locations[1].license_id | |
| locations[1].is_accepted | False |
| locations[1].is_published | False |
| locations[1].raw_source_name | Wind Energy Science, Vol 10, Pp 1137-1152 (2025) |
| locations[1].landing_page_url | https://doaj.org/article/80e476938a07408a995fc24056f55ad8 |
| indexed_in | crossref, doaj |
| authorships[0].author.id | https://openalex.org/A5099074140 |
| authorships[0].author.orcid | https://orcid.org/0000-0002-8503-0764 |
| authorships[0].author.display_name | Simon Daenens |
| authorships[0].author_position | first |
| authorships[0].raw_author_name | Simon Daenens |
| authorships[0].is_corresponding | False |
| authorships[1].author.id | https://openalex.org/A5028997809 |
| authorships[1].author.orcid | https://orcid.org/0000-0003-3036-617X |
| authorships[1].author.display_name | Timothy Verstraeten |
| authorships[1].author_position | middle |
| authorships[1].raw_author_name | Timothy Verstraeten |
| authorships[1].is_corresponding | False |
| authorships[2].author.id | https://openalex.org/A5048553817 |
| authorships[2].author.orcid | https://orcid.org/0000-0001-5659-0079 |
| authorships[2].author.display_name | Pieter-Jan Daems |
| authorships[2].author_position | middle |
| authorships[2].raw_author_name | Pieter-Jan Daems |
| authorships[2].is_corresponding | False |
| authorships[3].author.id | https://openalex.org/A5064553018 |
| authorships[3].author.orcid | https://orcid.org/0000-0001-6346-4564 |
| authorships[3].author.display_name | Ann Nowé |
| authorships[3].author_position | middle |
| authorships[3].raw_author_name | Ann Nowé |
| authorships[3].is_corresponding | False |
| authorships[4].author.id | https://openalex.org/A5053092945 |
| authorships[4].author.orcid | https://orcid.org/0000-0002-6574-7629 |
| authorships[4].author.display_name | Jan Helsen |
| authorships[4].author_position | last |
| authorships[4].raw_author_name | Jan Helsen |
| authorships[4].is_corresponding | False |
| has_content.pdf | True |
| has_content.grobid_xml | True |
| is_paratext | False |
| open_access.is_oa | True |
| open_access.oa_url | https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf |
| open_access.oa_status | gold |
| open_access.any_repository_has_fulltext | False |
| created_date | 2025-10-10T00:00:00 |
| display_name | Spatio-temporal graph neural networks for power prediction in offshore wind farms using SCADA data |
| has_fulltext | False |
| is_retracted | False |
| updated_date | 2025-11-25T14:43:58.451035 |
| primary_topic.id | https://openalex.org/T11052 |
| primary_topic.field.id | https://openalex.org/fields/22 |
| primary_topic.field.display_name | Engineering |
| primary_topic.score | 0.9994999766349792 |
| primary_topic.domain.id | https://openalex.org/domains/3 |
| primary_topic.domain.display_name | Physical Sciences |
| primary_topic.subfield.id | https://openalex.org/subfields/2208 |
| primary_topic.subfield.display_name | Electrical and Electronic Engineering |
| primary_topic.display_name | Energy Load and Power Forecasting |
| related_works | https://openalex.org/W2615977515, https://openalex.org/W2115760278, https://openalex.org/W2146396794, https://openalex.org/W4399194038, https://openalex.org/W2340591813, https://openalex.org/W4360902749, https://openalex.org/W962960785, https://openalex.org/W2888883444, https://openalex.org/W4389634106, https://openalex.org/W3093855182 |
| cited_by_count | 2 |
| counts_by_year[0].year | 2025 |
| counts_by_year[0].cited_by_count | 2 |
| locations_count | 2 |
| best_oa_location.id | doi:10.5194/wes-10-1137-2025 |
| best_oa_location.is_oa | True |
| best_oa_location.source.id | https://openalex.org/S4210189655 |
| best_oa_location.source.issn | 2366-7443, 2366-7451 |
| best_oa_location.source.type | journal |
| best_oa_location.source.is_oa | True |
| best_oa_location.source.issn_l | 2366-7443 |
| best_oa_location.source.is_core | True |
| best_oa_location.source.is_in_doaj | True |
| best_oa_location.source.display_name | Wind energy science |
| best_oa_location.source.host_organization | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_name | Copernicus Publications |
| best_oa_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| best_oa_location.source.host_organization_lineage_names | Copernicus Publications |
| best_oa_location.license | cc-by |
| best_oa_location.pdf_url | https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf |
| best_oa_location.version | publishedVersion |
| best_oa_location.raw_type | journal-article |
| best_oa_location.license_id | https://openalex.org/licenses/cc-by |
| best_oa_location.is_accepted | True |
| best_oa_location.is_published | True |
| best_oa_location.raw_source_name | Wind Energy Science |
| best_oa_location.landing_page_url | https://doi.org/10.5194/wes-10-1137-2025 |
| primary_location.id | doi:10.5194/wes-10-1137-2025 |
| primary_location.is_oa | True |
| primary_location.source.id | https://openalex.org/S4210189655 |
| primary_location.source.issn | 2366-7443, 2366-7451 |
| primary_location.source.type | journal |
| primary_location.source.is_oa | True |
| primary_location.source.issn_l | 2366-7443 |
| primary_location.source.is_core | True |
| primary_location.source.is_in_doaj | True |
| primary_location.source.display_name | Wind energy science |
| primary_location.source.host_organization | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_name | Copernicus Publications |
| primary_location.source.host_organization_lineage | https://openalex.org/P4310313756 |
| primary_location.source.host_organization_lineage_names | Copernicus Publications |
| primary_location.license | cc-by |
| primary_location.pdf_url | https://wes.copernicus.org/articles/10/1137/2025/wes-10-1137-2025.pdf |
| primary_location.version | publishedVersion |
| primary_location.raw_type | journal-article |
| primary_location.license_id | https://openalex.org/licenses/cc-by |
| primary_location.is_accepted | True |
| primary_location.is_published | True |
| primary_location.raw_source_name | Wind Energy Science |
| primary_location.landing_page_url | https://doi.org/10.5194/wes-10-1137-2025 |
| publication_date | 2025-06-25 |
| publication_year | 2025 |
| referenced_works | https://openalex.org/W2949676527, https://openalex.org/W4221167871, https://openalex.org/W4289656096, https://openalex.org/W3088315765, https://openalex.org/W4399488439, https://openalex.org/W4400129693, https://openalex.org/W4220719068, https://openalex.org/W4288419263, https://openalex.org/W2913284546, https://openalex.org/W4381568942, https://openalex.org/W3035649237, https://openalex.org/W3018589140, https://openalex.org/W3177301710, https://openalex.org/W4220770229, https://openalex.org/W2964621549, https://openalex.org/W6605049518, https://openalex.org/W2029749227, https://openalex.org/W4378077982, https://openalex.org/W2116341502, https://openalex.org/W4400590360, https://openalex.org/W4396627470, https://openalex.org/W4399488201, https://openalex.org/W2922683241, https://openalex.org/W3176900260, https://openalex.org/W2977311474, https://openalex.org/W3035831873, https://openalex.org/W3195017936 |
| referenced_works_count | 27 |
| abstract_inverted_index.% | 125, 141, 194, 281 |
| abstract_inverted_index.A | 47 |
| abstract_inverted_index.a | 4, 14, 18, 31, 54, 65, 82, 183, 287 |
| abstract_inverted_index.%, | 128, 144, 285 |
| abstract_inverted_index.30 | 23 |
| abstract_inverted_index.an | 189 |
| abstract_inverted_index.as | 30, 214 |
| abstract_inverted_index.at | 17 |
| abstract_inverted_index.by | 52 |
| abstract_inverted_index.in | 13, 93, 116, 131, 236, 258, 290 |
| abstract_inverted_index.is | 28, 50, 79, 204, 256 |
| abstract_inverted_index.of | 22, 57, 122, 138, 179, 187, 191, 241, 251, 274 |
| abstract_inverted_index.s. | 24 |
| abstract_inverted_index.to | 39, 74, 154, 157, 196, 247, 263, 282 |
| abstract_inverted_index.we | 231 |
| abstract_inverted_index.GNN | 106, 149, 270 |
| abstract_inverted_index.Our | 77 |
| abstract_inverted_index.The | 25, 96 |
| abstract_inverted_index.and | 103, 126, 130, 142, 162, 218, 238, 244 |
| abstract_inverted_index.for | 7, 88 |
| abstract_inverted_index.its | 155, 245 |
| abstract_inverted_index.the | 61, 75, 101, 104, 117, 132, 147, 166, 176, 197, 215, 233, 264, 268, 272 |
| abstract_inverted_index.0.02 | 284 |
| abstract_inverted_index.12.6 | 280 |
| abstract_inverted_index.20.7 | 140 |
| abstract_inverted_index.22.6 | 124 |
| abstract_inverted_index.30.3 | 127 |
| abstract_inverted_index.30.5 | 143 |
| abstract_inverted_index.57.4 | 193 |
| abstract_inverted_index.GNN. | 76 |
| abstract_inverted_index.This | 1, 202 |
| abstract_inverted_index.both | 100, 160 |
| abstract_inverted_index.farm | 16, 27 |
| abstract_inverted_index.from | 44, 279 |
| abstract_inverted_index.full | 177 |
| abstract_inverted_index.high | 19 |
| abstract_inverted_index.into | 60, 81 |
| abstract_inverted_index.just | 283 |
| abstract_inverted_index.long | 66 |
| abstract_inverted_index.loss | 91, 277 |
| abstract_inverted_index.mean | 118, 133 |
| abstract_inverted_index.rate | 273 |
| abstract_inverted_index.show | 98 |
| abstract_inverted_index.such | 213 |
| abstract_inverted_index.that | 99 |
| abstract_inverted_index.time | 55 |
| abstract_inverted_index.used | 38 |
| abstract_inverted_index.what | 255 |
| abstract_inverted_index.wind | 9, 15, 26, 94, 180 |
| abstract_inverted_index.with | 33, 171, 182 |
| abstract_inverted_index.(MAE) | 121 |
| abstract_inverted_index.(MLP) | 221 |
| abstract_inverted_index.(NBM) | 86 |
| abstract_inverted_index.being | 72 |
| abstract_inverted_index.curve | 112, 199, 217, 266 |
| abstract_inverted_index.error | 120, 136, 186, 226 |
| abstract_inverted_index.graph | 34 |
| abstract_inverted_index.input | 42, 58 |
| abstract_inverted_index.logs. | 261 |
| abstract_inverted_index.model | 6, 78, 85, 167 |
| abstract_inverted_index.novel | 5 |
| abstract_inverted_index.paper | 2 |
| abstract_inverted_index.power | 11, 90, 111, 198, 216, 229, 265, 276 |
| abstract_inverted_index.range | 178 |
| abstract_inverted_index.types | 250 |
| abstract_inverted_index.under | 207 |
| abstract_inverted_index.waked | 208 |
| abstract_inverted_index.where | 210 |
| abstract_inverted_index.(GNNs) | 37 |
| abstract_inverted_index.(LSTM) | 69 |
| abstract_inverted_index.(MAPE) | 137 |
| abstract_inverted_index.Beyond | 228 |
| abstract_inverted_index.across | 175 |
| abstract_inverted_index.around | 139 |
| abstract_inverted_index.before | 71 |
| abstract_inverted_index.beyond | 254 |
| abstract_inverted_index.energy | 173 |
| abstract_inverted_index.events | 92, 253, 278 |
| abstract_inverted_index.farms. | 95 |
| abstract_inverted_index.graph, | 32, 62 |
| abstract_inverted_index.higher | 225 |
| abstract_inverted_index.memory | 68 |
| abstract_inverted_index.models | 107, 222 |
| abstract_inverted_index.neural | 35 |
| abstract_inverted_index.normal | 83 |
| abstract_inverted_index.output | 12 |
| abstract_inverted_index.passed | 73 |
| abstract_inverted_index.rates. | 227 |
| abstract_inverted_index.ratios | 174 |
| abstract_inverted_index.series | 56 |
| abstract_inverted_index.status | 260 |
| abstract_inverted_index.0.0373, | 188 |
| abstract_inverted_index.ability | 156, 246 |
| abstract_inverted_index.average | 185 |
| abstract_inverted_index.binning | 200 |
| abstract_inverted_index.capture | 159 |
| abstract_inverted_index.events. | 293 |
| abstract_inverted_index.exhibit | 223 |
| abstract_inverted_index.feeding | 53 |
| abstract_inverted_index.method, | 267 |
| abstract_inverted_index.method. | 201 |
| abstract_inverted_index.methods | 212 |
| abstract_inverted_index.model's | 234 |
| abstract_inverted_index.network | 70 |
| abstract_inverted_index.reduced | 242 |
| abstract_inverted_index.reduces | 271 |
| abstract_inverted_index.results | 97 |
| abstract_inverted_index.spatial | 102, 161 |
| abstract_inverted_index.through | 64 |
| abstract_inverted_index.turbine | 10 |
| abstract_inverted_index.various | 249 |
| abstract_inverted_index.Compared | 262 |
| abstract_inverted_index.Notably, | 146 |
| abstract_inverted_index.abnormal | 252, 292 |
| abstract_inverted_index.absolute | 119, 134 |
| abstract_inverted_index.achieves | 168 |
| abstract_inverted_index.behavior | 84 |
| abstract_inverted_index.compared | 195 |
| abstract_inverted_index.features | 43, 59 |
| abstract_inverted_index.identify | 248 |
| abstract_inverted_index.methods, | 113 |
| abstract_inverted_index.networks | 36 |
| abstract_inverted_index.recorded | 257 |
| abstract_inverted_index.selected | 41 |
| abstract_inverted_index.standard | 259 |
| abstract_inverted_index.superior | 151 |
| abstract_inverted_index.temporal | 20, 48, 163 |
| abstract_inverted_index.weighted | 184 |
| abstract_inverted_index.Abstract. | 0 |
| abstract_inverted_index.achieving | 114 |
| abstract_inverted_index.advantage | 203 |
| abstract_inverted_index.aggregate | 40 |
| abstract_inverted_index.agreement | 170 |
| abstract_inverted_index.analyzing | 89, 239 |
| abstract_inverted_index.capturing | 291 |
| abstract_inverted_index.component | 49 |
| abstract_inverted_index.detecting | 237 |
| abstract_inverted_index.dynamics. | 164 |
| abstract_inverted_index.framework | 87 |
| abstract_inverted_index.instances | 240 |
| abstract_inverted_index.processed | 63 |
| abstract_inverted_index.turbines. | 46 |
| abstract_inverted_index.attributed | 153 |
| abstract_inverted_index.especially | 205 |
| abstract_inverted_index.illustrate | 232 |
| abstract_inverted_index.integrated | 80 |
| abstract_inverted_index.introduced | 51 |
| abstract_inverted_index.introduces | 3 |
| abstract_inverted_index.multilayer | 219 |
| abstract_inverted_index.outperform | 108 |
| abstract_inverted_index.percentage | 135 |
| abstract_inverted_index.perceptron | 220 |
| abstract_inverted_index.predicting | 8 |
| abstract_inverted_index.pronounced | 206 |
| abstract_inverted_index.reductions | 115 |
| abstract_inverted_index.remarkable | 169 |
| abstract_inverted_index.resolution | 21 |
| abstract_inverted_index.short-term | 67 |
| abstract_inverted_index.undetected | 275 |
| abstract_inverted_index.conditions, | 209 |
| abstract_inverted_index.data-driven | 110 |
| abstract_inverted_index.directions, | 181 |
| abstract_inverted_index.effectively | 158 |
| abstract_inverted_index.improvement | 190, 289 |
| abstract_inverted_index.neighboring | 45 |
| abstract_inverted_index.performance | 243 |
| abstract_inverted_index.prediction, | 230 |
| abstract_inverted_index.represented | 29 |
| abstract_inverted_index.substantial | 288 |
| abstract_inverted_index.traditional | 109, 211 |
| abstract_inverted_index.demonstrates | 150 |
| abstract_inverted_index.performance, | 152 |
| abstract_inverted_index.Additionally, | 165 |
| abstract_inverted_index.SCADA-derived | 172 |
| abstract_inverted_index.approximately | 123, 192 |
| abstract_inverted_index.demonstrating | 286 |
| abstract_inverted_index.effectiveness | 235 |
| abstract_inverted_index.respectively, | 129 |
| abstract_inverted_index.respectively. | 145 |
| abstract_inverted_index.significantly | 224 |
| abstract_inverted_index.spatio-temporal | 105, 148, 269 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 95 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.89587021 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |