{"id":"https://openalex.org/W1980746328","doi":"https://doi.org/10.1109/cdc.2013.6760016","title":"Large-scale probabilistic forecasting in energy systems using sparse Gaussian conditional random fields","display_name":"Large-scale probabilistic forecasting in energy systems using sparse Gaussian conditional random fields","publication_year":2013,"publication_date":"2013-12-01","ids":{"openalex":"https://openalex.org/W1980746328","doi":"https://doi.org/10.1109/cdc.2013.6760016","mag":"1980746328"},"language":"en","primary_location":{"id":"doi:10.1109/cdc.2013.6760016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2013.6760016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"52nd IEEE Conference on Decision and Control","raw_type":"proceedings-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/A5078643508","display_name":"Matt Wytock","orcid":null},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Matt Wytock","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","[Sch. of Comput. Sci., Carnegie, Mellon Univ., Pittsburgh, PA, USA]"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"[Sch. of Comput. Sci., Carnegie, Mellon Univ., Pittsburgh, PA, USA]","institution_ids":["https://openalex.org/I74973139"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5075035644","display_name":"J. Zico Kolter","orcid":"https://orcid.org/0000-0002-8106-5759"},"institutions":[{"id":"https://openalex.org/I74973139","display_name":"Carnegie Mellon University","ror":"https://ror.org/05x2bcf33","country_code":"US","type":"education","lineage":["https://openalex.org/I74973139"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"J. Zico Kolter","raw_affiliation_strings":["School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","[Sch. of Comput. Sci., Carnegie, Mellon Univ., Pittsburgh, PA, USA]"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, Carnegie Mellon University, Pittsburgh, Pennsylvania, USA","institution_ids":["https://openalex.org/I74973139"]},{"raw_affiliation_string":"[Sch. of Comput. Sci., Carnegie, Mellon Univ., Pittsburgh, PA, USA]","institution_ids":["https://openalex.org/I74973139"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5078643508"],"corresponding_institution_ids":["https://openalex.org/I74973139"],"apc_list":null,"apc_paid":null,"fwci":2.8828,"has_fulltext":false,"cited_by_count":37,"citation_normalized_percentile":{"value":0.9112657,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"9","issue":null,"first_page":"1019","last_page":"1024"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9993000030517578,"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":0.9993000030517578,"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/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9750000238418579,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.961899995803833,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/probabilistic-logic","display_name":"Probabilistic logic","score":0.7006376385688782},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6453997492790222},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.6385279893875122},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.5919872522354126},{"id":"https://openalex.org/keywords/wind-power","display_name":"Wind power","score":0.5365917682647705},{"id":"https://openalex.org/keywords/electric-power-system","display_name":"Electric power system","score":0.4632403254508972},{"id":"https://openalex.org/keywords/conditional-probability-distribution","display_name":"Conditional probability distribution","score":0.45143136382102966},{"id":"https://openalex.org/keywords/conditional-random-field","display_name":"Conditional random field","score":0.43177908658981323},{"id":"https://openalex.org/keywords/probability-distribution","display_name":"Probability distribution","score":0.4165370464324951},{"id":"https://openalex.org/keywords/mathematical-optimization","display_name":"Mathematical optimization","score":0.4035413861274719},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29453688859939575},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.2719023823738098},{"id":"https://openalex.org/keywords/power","display_name":"Power (physics)","score":0.18922844529151917},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.16210055351257324},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.11164075136184692},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11135116219520569}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7006376385688782},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6453997492790222},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.6385279893875122},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.5919872522354126},{"id":"https://openalex.org/C78600449","wikidata":"https://www.wikidata.org/wiki/Q43302","display_name":"Wind power","level":2,"score":0.5365917682647705},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.4632403254508972},{"id":"https://openalex.org/C43555835","wikidata":"https://www.wikidata.org/wiki/Q2300258","display_name":"Conditional probability distribution","level":2,"score":0.45143136382102966},{"id":"https://openalex.org/C152565575","wikidata":"https://www.wikidata.org/wiki/Q1124538","display_name":"Conditional random field","level":2,"score":0.43177908658981323},{"id":"https://openalex.org/C149441793","wikidata":"https://www.wikidata.org/wiki/Q200726","display_name":"Probability distribution","level":2,"score":0.4165370464324951},{"id":"https://openalex.org/C126255220","wikidata":"https://www.wikidata.org/wiki/Q141495","display_name":"Mathematical optimization","level":1,"score":0.4035413861274719},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29453688859939575},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2719023823738098},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.18922844529151917},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.16210055351257324},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.11164075136184692},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11135116219520569},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","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},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","level":1,"score":0.0}],"mesh":[],"locations_count":3,"locations":[{"id":"doi:10.1109/cdc.2013.6760016","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cdc.2013.6760016","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"52nd IEEE Conference on Decision and Control","raw_type":"proceedings-article"},{"id":"pmh:oai:repository.cmu.edu:compsci-3680","is_oa":false,"landing_page_url":"http://repository.cmu.edu/cgi/viewcontent.cgi?article=3680&context=compsci","pdf_url":null,"source":{"id":"https://openalex.org/S4306400668","display_name":"Research Showcase @ Carnegie Mellon University (Carnegie Mellon University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I74973139","host_organization_name":"Carnegie Mellon University","host_organization_lineage":["https://openalex.org/I74973139"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Computer Science Department","raw_type":"text"},{"id":"pmh:doi:10.1184/r1/6606773","is_oa":false,"landing_page_url":null,"pdf_url":null,"source":{"id":"https://openalex.org/S4406922384","display_name":"Open MIND","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Journal contribution"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Affordable and clean energy","score":0.8500000238418579,"id":"https://metadata.un.org/sdg/7"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":31,"referenced_works":["https://openalex.org/W107400255","https://openalex.org/W138335974","https://openalex.org/W1501916037","https://openalex.org/W1899679829","https://openalex.org/W1966041919","https://openalex.org/W2005898567","https://openalex.org/W2079076142","https://openalex.org/W2116150816","https://openalex.org/W2129999749","https://openalex.org/W2132555912","https://openalex.org/W2137892504","https://openalex.org/W2151128232","https://openalex.org/W2162602792","https://openalex.org/W2163614729","https://openalex.org/W2187207171","https://openalex.org/W2302787989","https://openalex.org/W2623593934","https://openalex.org/W2953317318","https://openalex.org/W3126081667","https://openalex.org/W3170861794","https://openalex.org/W3211192464","https://openalex.org/W4206192903","https://openalex.org/W6604342815","https://openalex.org/W6605583707","https://openalex.org/W6630046802","https://openalex.org/W6640015824","https://openalex.org/W6677479583","https://openalex.org/W6680564993","https://openalex.org/W6682227116","https://openalex.org/W6687250854","https://openalex.org/W7044268886"],"related_works":["https://openalex.org/W2356597680","https://openalex.org/W2114846443","https://openalex.org/W2093471820","https://openalex.org/W3102147106","https://openalex.org/W2347460059","https://openalex.org/W50079190","https://openalex.org/W3136048405","https://openalex.org/W4221138922","https://openalex.org/W1539131693","https://openalex.org/W162901985"],"abstract_inverted_index":{"Short-term":[0],"forecasting":[1,14,42,83,101],"is":[2,23,55],"a":[3,7,30,73,98,128],"ubiquitous":[4],"practice":[5,45],"in":[6,44,127],"wide":[8],"range":[9],"of":[10,116,121],"energy":[11],"systems,":[12],"including":[13],"demand,":[15],"renewable":[16],"generation,":[17],"and":[18,39,65,86],"electricity":[19],"pricing.":[20],"Although":[21],"it":[22,54,88],"known":[24],"that":[25,105],"probabilistic":[26,107],"forecasts":[27],"(which":[28],"give":[29],"distribution":[31],"over":[32,62],"possible":[33],"future":[34],"outcomes)":[35],"can":[36],"improve":[37],"planning":[38],"control,":[40],"many":[41],"systems":[43],"are":[46],"just":[47],"used":[48],"as":[49,53],"\u201cpoint":[50],"forecast\u201d":[51],"tools,":[52],"challenging":[56],"to":[57,82,89],"represent":[58],"high-dimensional":[59,78],"non-Gaussian":[60,91],"distributions":[61,81,120],"multiple":[63],"spatial":[64],"temporal":[66],"points.":[67],"In":[68],"this":[69,106],"paper,":[70],"we":[71,103],"apply":[72],"recently-proposed":[74],"algorithm":[75],"for":[76,132],"modeling":[77,118],"conditional":[79],"Gaussian":[80],"wind":[84,99],"power":[85,100,122],"extend":[87],"the":[90,94,114],"case":[92],"using":[93],"copula":[95],"transform.":[96],"On":[97],"task,":[102],"show":[104],"model":[108],"greatly":[109],"outperforms":[110],"other":[111],"methods":[112],"on":[113],"task":[115],"accurately":[117],"potential":[119],"(as":[123],"would":[124],"be":[125],"necessary":[126],"stochastic":[129],"dispatch":[130],"problem,":[131],"example).":[133]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":2},{"year":2022,"cited_by_count":2},{"year":2021,"cited_by_count":2},{"year":2020,"cited_by_count":3},{"year":2019,"cited_by_count":3},{"year":2018,"cited_by_count":3},{"year":2017,"cited_by_count":8},{"year":2016,"cited_by_count":7},{"year":2015,"cited_by_count":4},{"year":2014,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}
