{"id":"https://openalex.org/W4411345922","doi":"https://doi.org/10.1145/3679240.3734623","title":"Probabilistic and Explainable Machine Learning for Tabular Power Grid Data","display_name":"Probabilistic and Explainable Machine Learning for Tabular Power Grid Data","publication_year":2025,"publication_date":"2025-06-16","ids":{"openalex":"https://openalex.org/W4411345922","doi":"https://doi.org/10.1145/3679240.3734623"},"language":"en","primary_location":{"id":"doi:10.1145/3679240.3734623","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3679240.3734623","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3679240.3734623","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://dl.acm.org/doi/pdf/10.1145/3679240.3734623","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5118214099","display_name":"Alexandra Nikoltchovska","orcid":null},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Alexandra Nikoltchovska","raw_affiliation_strings":["Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5045498666","display_name":"Sebastian P\u00fctz","orcid":"https://orcid.org/0009-0009-8468-4166"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Sebastian P\u00fctz","raw_affiliation_strings":["Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103659615","display_name":"Li Xiao","orcid":"https://orcid.org/0000-0002-7381-8685"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Xiao Li","raw_affiliation_strings":["Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014228448","display_name":"Veit Hagenmeyer","orcid":"https://orcid.org/0000-0002-3572-9083"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Veit Hagenmeyer","raw_affiliation_strings":["Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040295886","display_name":"Benjamin Sch\u00e4fer","orcid":"https://orcid.org/0000-0003-1607-9748"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Benjamin Sch\u00e4fer","raw_affiliation_strings":["Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology, Eggenstein-Leopoldshafen, Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":5,"corresponding_author_ids":["https://openalex.org/A5118214099"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":1.3291,"has_fulltext":true,"cited_by_count":2,"citation_normalized_percentile":{"value":0.81741497,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":98,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"213","last_page":"231"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9950000047683716,"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":0.9950000047683716,"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/T13650","display_name":"Computational Physics and Python Applications","score":0.993399977684021,"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/T14280","display_name":"Big Data Technologies and Applications","score":0.9868000149726868,"subfield":{"id":"https://openalex.org/subfields/1802","display_name":"Information Systems and Management"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.8011459112167358},{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.733241856098175},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5276792645454407},{"id":"https://openalex.org/keywords/power-grid","display_name":"Power grid","score":0.5015442371368408},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.49619847536087036},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.440306156873703},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.40870535373687744}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8011459112167358},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.733241856098175},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5276792645454407},{"id":"https://openalex.org/C2983254600","wikidata":"https://www.wikidata.org/wiki/Q1096907","display_name":"Power grid","level":3,"score":0.5015442371368408},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49619847536087036},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.440306156873703},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.40870535373687744},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","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},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3679240.3734623","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3679240.3734623","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3679240.3734623","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3679240.3734623","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3679240.3734623","pdf_url":"https://dl.acm.org/doi/pdf/10.1145/3679240.3734623","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 16th ACM International Conference on Future and Sustainable Energy Systems","raw_type":"proceedings-article"},"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.7400000095367432,"id":"https://metadata.un.org/sdg/7"}],"awards":[{"id":"https://openalex.org/G2044937970","display_name":null,"funder_award_id":"VH-NG-1727","funder_id":"https://openalex.org/F4320325698","funder_display_name":"Helmholtz Association"}],"funders":[{"id":"https://openalex.org/F4320320875","display_name":"Deutscher Akademischer Austauschdienst","ror":"https://ror.org/039djdh30"},{"id":"https://openalex.org/F4320322725","display_name":"China Scholarship Council","ror":"https://ror.org/04atp4p48"},{"id":"https://openalex.org/F4320325698","display_name":"Helmholtz Association","ror":null},{"id":"https://openalex.org/F4320332086","display_name":"Helmholtz Artificial Intelligence Cooperation Unit","ror":null}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W4411345922.pdf","grobid_xml":"https://content.openalex.org/works/W4411345922.grobid-xml"},"referenced_works_count":35,"referenced_works":["https://openalex.org/W204900244","https://openalex.org/W1560021816","https://openalex.org/W1583837637","https://openalex.org/W1984113680","https://openalex.org/W2025720061","https://openalex.org/W2050955352","https://openalex.org/W2082137964","https://openalex.org/W2612690371","https://openalex.org/W2759719643","https://openalex.org/W2807259040","https://openalex.org/W2889151444","https://openalex.org/W2924483711","https://openalex.org/W2990138404","https://openalex.org/W3008710632","https://openalex.org/W3034511969","https://openalex.org/W3039576359","https://openalex.org/W3099919647","https://openalex.org/W3112797269","https://openalex.org/W3170720134","https://openalex.org/W3174086521","https://openalex.org/W3203286420","https://openalex.org/W3216660278","https://openalex.org/W4220699563","https://openalex.org/W4286231567","https://openalex.org/W4288408957","https://openalex.org/W4307225838","https://openalex.org/W4372283338","https://openalex.org/W4382362923","https://openalex.org/W4387344016","https://openalex.org/W4387974751","https://openalex.org/W4391825320","https://openalex.org/W4398188597","https://openalex.org/W4400446495","https://openalex.org/W4404078864","https://openalex.org/W4404953652"],"related_works":["https://openalex.org/W2943623134","https://openalex.org/W2494523064","https://openalex.org/W2215759665","https://openalex.org/W2030292806","https://openalex.org/W2960358116","https://openalex.org/W2938171715","https://openalex.org/W4287727129","https://openalex.org/W3041172967","https://openalex.org/W2749065928","https://openalex.org/W2147155098"],"abstract_inverted_index":{"Modeling":[0],"power":[1,32,69,93,222],"grid":[2,70,94,126],"frequency":[3,95],"stability":[4,127,181,218],"is":[5,55],"becoming":[6],"increasingly":[7],"challenging":[8],"due":[9],"to":[10,61,89,110,121],"the":[11,28,43,47,65,77,123,142,173,202],"integration":[12],"of":[13,31,45,68,79,103,131],"renewable":[14,189],"energy":[15],"sources.Machine":[16],"learning":[17,83],"approaches,":[18],"such":[19],"as":[20,167,169,192],"gradient-boosted":[21],"trees,":[22],"have":[23],"shown":[24],"promise":[25],"in":[26,133,183,196,201],"analyzing":[27],"complex":[29],"characteristics":[30],"systems.However,":[33],"these":[34,112],"models":[35],"are":[36,172],"inherently":[37],"deterministic,":[38],"providing":[39,156],"only":[40],"point":[41],"estimates.Meanwhile,":[42],"task":[44],"capturing":[46],"underlying":[48],"uncertainty,":[49,199],"particularly":[50],"through":[51],"(deep)":[52],"probabilistic":[53,101],"models,":[54,114],"still":[56],"underexplored,":[57],"despite":[58],"its":[59],"potential":[60],"better":[62],"account":[63],"for":[64,86,91,176,213,220],"stochastic":[66],"nature":[67],"dynamics.In":[71],"this":[72],"paper,":[73],"we":[74,115],"first":[75],"compare":[76],"performance":[78,151],"TabNet,":[80,104],"a":[81,100,193],"deep":[82],"architecture":[84],"designed":[85],"tabular":[87],"data,":[88],"XGBoost":[90],"modeling":[92,177],"stability.We":[96],"then":[97],"present":[98],"TabNetProba:":[99],"extension":[102],"that":[105,147,161],"enables":[106],"uncertainty-aware":[107],"estimates":[108,219],"comparable":[109],"NGBoost.Using":[111],"(trained)":[113],"leverage":[116],"explainable":[117],"artificial":[118],"intelligence":[119],"(XAI)":[120],"analyze":[122],"drivers":[124],"influencing":[125],"and":[128,141,163,178,206,216],"identify":[129],"sources":[130],"uncertainty":[132,158,210],"two":[134],"major":[135],"European":[136],"synchronous":[137,185],"areas:":[138],"Continental":[139,187],"Europe":[140],"Nordic":[143,203],"region.Our":[144],"results":[145],"demonstrate":[146],"TabNetProba":[148],"achieves":[149],"competitive":[150],"with":[152],"state-of-the-art":[153],"methods":[154],"while":[155,200],"reliable":[157,215],"estimates.We":[159],"find":[160],"load":[162,205],"conventional":[164],"generation":[165,190,207],"ramps,":[166],"well":[168],"forecast":[170],"errors,":[171],"key":[174,194],"quantities":[175],"explaining":[179,197],"mean":[180],"indicators":[182],"both":[184],"areas.In":[186],"Europe,":[188],"emerges":[191],"factor":[195],"model":[198],"region,":[204],"features":[208],"dominate":[209],"estimation,":[211],"allowing":[212],"more":[214],"interpretable":[217],"modern":[221],"systems.":[223]},"counts_by_year":[{"year":2026,"cited_by_count":2}],"updated_date":"2026-04-21T08:09:41.155169","created_date":"2025-10-10T00:00:00"}