{"id":"https://openalex.org/W4328007185","doi":"https://doi.org/10.1109/jsyst.2023.3250403","title":"Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power","display_name":"Geometric Deep-Learning-Based Spatiotemporal Forecasting for Inverter-Based Solar Power","publication_year":2023,"publication_date":"2023-03-20","ids":{"openalex":"https://openalex.org/W4328007185","doi":"https://doi.org/10.1109/jsyst.2023.3250403"},"language":"en","primary_location":{"id":"doi:10.1109/jsyst.2023.3250403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsyst.2023.3250403","pdf_url":null,"source":{"id":"https://openalex.org/S95999327","display_name":"IEEE Systems Journal","issn_l":"1932-8184","issn":["1932-8184","1937-9234","2373-7816"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Systems Journal","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5032464575","display_name":"Chuan Qin","orcid":"https://orcid.org/0000-0001-7804-3690"},"institutions":[{"id":"https://openalex.org/I142606810","display_name":"Pacific Northwest National Laboratory","ror":"https://ror.org/05h992307","country_code":"US","type":"facility","lineage":["https://openalex.org/I1325736334","https://openalex.org/I1330989302","https://openalex.org/I142606810","https://openalex.org/I39565521"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Chuan Qin","raw_affiliation_strings":["Pacific Northwest National Laboratory, Richland, WA, USA"],"affiliations":[{"raw_affiliation_string":"Pacific Northwest National Laboratory, Richland, WA, USA","institution_ids":["https://openalex.org/I142606810"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028171150","display_name":"Anurag K. Srivastava","orcid":"https://orcid.org/0000-0003-3518-8018"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anurag K. Srivastava","raw_affiliation_strings":["West Virginia University, Morgantown, WV, USA"],"affiliations":[{"raw_affiliation_string":"West Virginia University, Morgantown, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058690742","display_name":"Ahmed Yousuf Saber","orcid":"https://orcid.org/0000-0002-4820-0242"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Ahmed Y. Saber","raw_affiliation_strings":["Electrical Transient Analyzer Program, Irvine, CA, USA"],"affiliations":[{"raw_affiliation_string":"Electrical Transient Analyzer Program, Irvine, CA, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5029718049","display_name":"Dax Matthews","orcid":"https://orcid.org/0000-0002-8980-9265"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dax Matthews","raw_affiliation_strings":["Hawai'i Natural Energy Institute, Honolulu, HI, USA"],"affiliations":[{"raw_affiliation_string":"Hawai'i Natural Energy Institute, Honolulu, HI, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012242278","display_name":"Kevin L. Davies","orcid":"https://orcid.org/0000-0003-2081-889X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Kevin Davies","raw_affiliation_strings":["Hawai'i Natural Energy Institute, Honolulu, HI, USA"],"affiliations":[{"raw_affiliation_string":"Hawai'i Natural Energy Institute, Honolulu, HI, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5032464575"],"corresponding_institution_ids":["https://openalex.org/I142606810"],"apc_list":null,"apc_paid":null,"fwci":2.9574,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.92597781,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":"17","issue":"3","first_page":"3425","last_page":"3435"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":0.9998999834060669,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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/T11019","display_name":"Image Enhancement Techniques","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.7259204387664795},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.5664420127868652},{"id":"https://openalex.org/keywords/renewable-energy","display_name":"Renewable energy","score":0.5044180154800415},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4315497875213623},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.4221786856651306},{"id":"https://openalex.org/keywords/reliability-engineering","display_name":"Reliability engineering","score":0.3867557644844055},{"id":"https://openalex.org/keywords/real-time-computing","display_name":"Real-time computing","score":0.34583741426467896},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.3057289123535156},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.2894752621650696},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2583877444267273},{"id":"https://openalex.org/keywords/electrical-engineering","display_name":"Electrical engineering","score":0.13960519433021545}],"concepts":[{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.7259204387664795},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5664420127868652},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.5044180154800415},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4315497875213623},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.4221786856651306},{"id":"https://openalex.org/C200601418","wikidata":"https://www.wikidata.org/wiki/Q2193887","display_name":"Reliability engineering","level":1,"score":0.3867557644844055},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.34583741426467896},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.3057289123535156},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2894752621650696},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2583877444267273},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.13960519433021545},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/jsyst.2023.3250403","is_oa":false,"landing_page_url":"https://doi.org/10.1109/jsyst.2023.3250403","pdf_url":null,"source":{"id":"https://openalex.org/S95999327","display_name":"IEEE Systems Journal","issn_l":"1932-8184","issn":["1932-8184","1937-9234","2373-7816"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319808","host_organization_name":"Institute of Electrical and Electronics Engineers","host_organization_lineage":["https://openalex.org/P4310319808"],"host_organization_lineage_names":["Institute of Electrical and Electronics Engineers"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"IEEE Systems Journal","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.6200000047683716,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G5166408565","display_name":null,"funder_award_id":"DE-IA0000025","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"},{"id":"https://openalex.org/G7216477041","display_name":null,"funder_award_id":"DE-OE000878","funder_id":"https://openalex.org/F4320306084","funder_display_name":"U.S. Department of Energy"}],"funders":[{"id":"https://openalex.org/F4320306084","display_name":"U.S. Department of Energy","ror":"https://ror.org/01bj3aw27"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":33,"referenced_works":["https://openalex.org/W130406184","https://openalex.org/W1587194470","https://openalex.org/W1970463904","https://openalex.org/W1989726836","https://openalex.org/W2034404038","https://openalex.org/W2061317708","https://openalex.org/W2080822545","https://openalex.org/W2081770709","https://openalex.org/W2101491865","https://openalex.org/W2109842007","https://openalex.org/W2115294291","https://openalex.org/W2168519756","https://openalex.org/W2185622477","https://openalex.org/W2412523952","https://openalex.org/W2607339923","https://openalex.org/W2621841909","https://openalex.org/W2752901932","https://openalex.org/W2758301547","https://openalex.org/W2762797296","https://openalex.org/W2764018574","https://openalex.org/W2782267877","https://openalex.org/W2786806645","https://openalex.org/W2790866531","https://openalex.org/W2909194704","https://openalex.org/W2943809873","https://openalex.org/W2963358464","https://openalex.org/W2979735839","https://openalex.org/W3015024348","https://openalex.org/W3024537702","https://openalex.org/W3043337086","https://openalex.org/W3112294296","https://openalex.org/W3164346365","https://openalex.org/W6746015598"],"related_works":["https://openalex.org/W2386968573","https://openalex.org/W2395064349","https://openalex.org/W2034374297","https://openalex.org/W2766130412","https://openalex.org/W2382628689","https://openalex.org/W2526730640","https://openalex.org/W2351171996","https://openalex.org/W2155742919","https://openalex.org/W2532099551","https://openalex.org/W1914681266"],"abstract_inverted_index":{"Conventional":[0],"power":[1,65,103],"grids":[2],"dominated":[3],"by":[4,47,138],"synchronous":[5],"generators":[6],"gradually":[7],"shift":[8],"to":[9,37,88,118,170],"variable":[10],"renewable-energy-integrated":[11],"grids.":[12],"With":[13],"the":[14,40,57,69,140,161,166,172],"growth":[15],"of":[16,62,73,109,151,199],"building-level":[17],"photovoltaic":[18],"(PV)":[19],"panels":[20],"and":[21,32,43,59,79,116,194,203],"other":[22],"inverter-based":[23],"resource":[24],"(IBR)":[25],"deployments":[26],"in":[27,84,111,144,206],"recent":[28],"years,":[29],"market":[30],"retailers":[31],"distribution":[33,75,114,209],"operators":[34],"have":[35],"had":[36],"deal":[38],"with":[39,81,157,165,176],"additional":[41],"operational":[42],"management":[44,78],"challenges":[45],"posed":[46],"unobserved":[48],"energy":[49],"flow":[50],"if":[51],"its":[52],"behind-the-meter":[53],"(BTM)":[54],"configuration.":[55],"Also,":[56],"intermittent":[58],"stochastic":[60],"nature":[61],"IBR-based":[63],"solar":[64],"introduces":[66],"uncertainty":[67,83],"into":[68],"net":[70,167],"load":[71,85,168,174,188,204],"forecasting":[72,126,169,175,183,189],"electric":[74],"systems.":[76],"Hence,":[77],"control":[80],"high":[82],"prediction":[86],"due":[87],"unobservable":[89],"PV":[90,102,125,154,182,202],"is":[91,128],"a":[92,106,112,145,197,207],"technical":[93],"challenge.":[94],"This":[95],"article":[96],"presents":[97],"deep-learning-based":[98],"algorithms":[99],"for":[100],"BTM":[101,124,153,181,201],"generation":[104],"using":[105,196],"limited":[107],"number":[108],"sensors":[110],"given":[113],"system":[115],"extending":[117],"adjacent":[119],"geographical":[120],"areas.":[121],"The":[122,149,179],"proposed":[123],"method":[127,184],"based":[129],"on":[130],"geometric":[131],"deep":[132],"learning\u2014a":[133],"spatiotemporal":[134],"graph":[135,147],"neural":[136],"network":[137],"processing":[139],"relationship":[141],"between":[142],"data":[143,162],"non-Euclidean":[146],"structure.":[148],"predictions":[150],"short-term":[152],"are":[155],"aggregated":[156],"loss":[158],"estimation":[159],"at":[160],"aggregation":[163],"point":[164],"compute":[171],"true":[173,187],"superior":[177],"performance.":[178],"developed":[180],"significantly":[185],"improved":[186],"results,":[190],"which":[191],"were":[192],"validated":[193],"analyzed":[195],"collection":[198],"actual":[200],"measurements":[205],"test":[208],"feeder.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":3}],"updated_date":"2026-03-27T05:58:40.876381","created_date":"2025-10-10T00:00:00"}