{"id":"https://openalex.org/W3129024273","doi":"https://doi.org/10.14428/esann/2021.es2021-25","title":"Deep Graph Convolutional Networks for Wind Speed Prediction","display_name":"Deep Graph Convolutional Networks for Wind Speed Prediction","publication_year":2021,"publication_date":"2021-01-01","ids":{"openalex":"https://openalex.org/W3129024273","doi":"https://doi.org/10.14428/esann/2021.es2021-25","mag":"3129024273"},"language":"en","primary_location":{"id":"doi:10.14428/esann/2021.es2021-25","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-25","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-25","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"bronze","oa_url":"https://doi.org/10.14428/esann/2021.es2021-25","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5038920217","display_name":"Tomasz Sta\u0144czyk","orcid":"https://orcid.org/0000-0003-0065-9668"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Tomasz Sta\u0144czyk","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5076867569","display_name":"Siamak Mehrkanoon","orcid":"https://orcid.org/0000-0002-0516-0391"},"institutions":[{"id":"https://openalex.org/I34352273","display_name":"Maastricht University","ror":"https://ror.org/02jz4aj89","country_code":"NL","type":"education","lineage":["https://openalex.org/I34352273"]}],"countries":["NL"],"is_corresponding":false,"raw_author_name":"Siamak Mehrkanoon","raw_affiliation_strings":["Maastricht University -Department of Data Science and Knowledge Engineering Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Maastricht University -Department of Data Science and Knowledge Engineering Paul-Henri Spaaklaan 1, 6229 EN Maastricht -The Netherlands","institution_ids":["https://openalex.org/I34352273"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":5,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"147","last_page":"152"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9987000226974487,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.9954000115394592,"subfield":{"id":"https://openalex.org/subfields/2215","display_name":"Building and Construction"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/adjacency-matrix","display_name":"Adjacency matrix","score":0.8356359601020813},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.6685375571250916},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6554297208786011},{"id":"https://openalex.org/keywords/adjacency-list","display_name":"Adjacency list","score":0.6358703374862671},{"id":"https://openalex.org/keywords/convolution","display_name":"Convolution (computer science)","score":0.5377889275550842},{"id":"https://openalex.org/keywords/wind-speed","display_name":"Wind speed","score":0.5179316401481628},{"id":"https://openalex.org/keywords/connection","display_name":"Connection (principal bundle)","score":0.49397140741348267},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4254327118396759},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3513221740722656},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.32564347982406616},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.3207826614379883},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1856793761253357},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.17486608028411865},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.15523144602775574},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12852662801742554}],"concepts":[{"id":"https://openalex.org/C180356752","wikidata":"https://www.wikidata.org/wiki/Q727035","display_name":"Adjacency matrix","level":3,"score":0.8356359601020813},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.6685375571250916},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6554297208786011},{"id":"https://openalex.org/C110484373","wikidata":"https://www.wikidata.org/wiki/Q264398","display_name":"Adjacency list","level":2,"score":0.6358703374862671},{"id":"https://openalex.org/C45347329","wikidata":"https://www.wikidata.org/wiki/Q5166604","display_name":"Convolution (computer science)","level":3,"score":0.5377889275550842},{"id":"https://openalex.org/C161067210","wikidata":"https://www.wikidata.org/wiki/Q1464943","display_name":"Wind speed","level":2,"score":0.5179316401481628},{"id":"https://openalex.org/C13355873","wikidata":"https://www.wikidata.org/wiki/Q2920850","display_name":"Connection (principal bundle)","level":2,"score":0.49397140741348267},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4254327118396759},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3513221740722656},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.32564347982406616},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.3207826614379883},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1856793761253357},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.17486608028411865},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.15523144602775574},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12852662801742554},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":6,"locations":[{"id":"doi:10.14428/esann/2021.es2021-25","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-25","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-25","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"},{"id":"pmh:oai:cris.maastrichtuniversity.nl:openaire/6add259f-cced-4911-8a2a-d9914151ce5c","is_oa":true,"landing_page_url":"https://cris.maastrichtuniversity.nl/en/publications/6add259f-cced-4911-8a2a-d9914151ce5c","pdf_url":null,"source":{"id":"https://openalex.org/S4306402616","display_name":"Research Publications (Maastricht University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I34352273","host_organization_name":"Maastricht University","host_organization_lineage":["https://openalex.org/I34352273"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Stanczyk, T & Mehrkanoon, S 2021, Deep Graph Convolutional Networks for Wind Speed Prediction. in European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN). i6doc, pp. 147-152, 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, Belgium, 6/10/21. https://doi.org/10.14428/esann/2021.ES2021-25","raw_type":"info:eu-repo/semantics/publishedVersion"},{"id":"pmh:oai:arXiv.org:2101.10041","is_oa":true,"landing_page_url":"http://arxiv.org/abs/2101.10041","pdf_url":"https://arxiv.org/pdf/2101.10041","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"","raw_type":"text"},{"id":"mag:3129024273","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/2101.10041v1","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"pmh:uu:oai:dspace.library.uu.nl:1874/423171","is_oa":true,"landing_page_url":"https://dspace.library.uu.nl/handle/1874/423171","pdf_url":null,"source":{"id":"https://openalex.org/S4306401843","display_name":"Data Archiving and Networked Services (DANS)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I1322597698","host_organization_name":"Royal Netherlands Academy of Arts and Sciences","host_organization_lineage":["https://openalex.org/I1322597698"],"host_organization_lineage_names":[],"type":"repository"},"license":"other-oa","license_id":"https://openalex.org/licenses/other-oa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ESANN 2021 Proceedings - 29th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning, 147. i6doc.com publication","raw_type":"info:eu-repo/semantics/bookpart"},{"id":"doi:10.48550/arxiv.2101.10041","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2101.10041","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.14428/esann/2021.es2021-25","is_oa":true,"landing_page_url":"https://doi.org/10.14428/esann/2021.es2021-25","pdf_url":"https://doi.org/10.14428/esann/2021.es2021-25","source":{"id":"https://openalex.org/S4306509709","display_name":"ESANN 2021 proceedings","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"ESANN 2021 proceedings","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7","score":0.8600000143051147}],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3129024273.pdf","grobid_xml":"https://content.openalex.org/works/W3129024273.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W1485009520","https://openalex.org/W1789155650","https://openalex.org/W1993512681","https://openalex.org/W2064675550","https://openalex.org/W2290960045","https://openalex.org/W2519887557","https://openalex.org/W2570329023","https://openalex.org/W2597066514","https://openalex.org/W2751808960","https://openalex.org/W2766856748","https://openalex.org/W2771185254","https://openalex.org/W2784435047","https://openalex.org/W2788667846","https://openalex.org/W2791891314","https://openalex.org/W2796341166","https://openalex.org/W2803865273","https://openalex.org/W2807252330","https://openalex.org/W2900615125","https://openalex.org/W2907941610","https://openalex.org/W2909877301","https://openalex.org/W2919115771","https://openalex.org/W2919278763","https://openalex.org/W2935316086","https://openalex.org/W2943852858","https://openalex.org/W2951766250","https://openalex.org/W2985331920","https://openalex.org/W3044719145","https://openalex.org/W3100761977","https://openalex.org/W3103720336"],"related_works":["https://openalex.org/W3207278177","https://openalex.org/W3198263212","https://openalex.org/W2965184053","https://openalex.org/W3099189760","https://openalex.org/W2964621549","https://openalex.org/W2799789854","https://openalex.org/W3164376315","https://openalex.org/W3115500183","https://openalex.org/W3117375996","https://openalex.org/W2982353995","https://openalex.org/W3160023773","https://openalex.org/W3013476156","https://openalex.org/W3138337643","https://openalex.org/W255663192","https://openalex.org/W3007438545","https://openalex.org/W2897247459","https://openalex.org/W3207512982","https://openalex.org/W2898512177","https://openalex.org/W2958577286","https://openalex.org/W186433452"],"abstract_inverted_index":{"In":[0],"this":[1],"paper,":[2],"we":[3],"introduce":[4],"a":[5,26,29],"new":[6],"model":[7,86],"for":[8],"wind":[9],"speed":[10],"prediction":[11],"based":[12,42],"on":[13,43,69,92],"spatio-temporal":[14],"graph":[15,27],"convolutional":[16],"networks.":[17],"Here,":[18],"weather":[19,46,74],"stations":[20,41,75],"are":[21],"treated":[22],"as":[23],"nodes":[24],"of":[25,37],"with":[28],"learnable":[30,65],"adjacency":[31,56],"matrix,":[32],"which":[33],"determines":[34],"the":[35,40,44,54,80,93],"strength":[36,60],"relations":[38],"between":[39],"historical":[45],"data.":[47],"The":[48],"self-loop":[49],"connection":[50],"is":[51,61],"added":[52],"to":[53],"learnt":[55],"matrix":[57],"and":[58,79],"its":[59],"controlled":[62],"by":[63],"additional":[64],"parameter.":[66],"Experiments":[67],"performed":[68],"real":[70],"datasets":[71],"collected":[72],"from":[73],"located":[76],"in":[77],"Denmark":[78],"Netherlands":[81],"show":[82],"that":[83],"our":[84],"proposed":[85],"outperforms":[87],"previously":[88],"developed":[89],"baseline":[90],"models":[91],"referenced":[94],"datasets.":[95]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":2}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
