{"id":"https://openalex.org/W7225375608","doi":"https://doi.org/10.5281/zenodo.24001028","title":"Carbon-Intensity Predictive Computing for Generative AI: Forecasting Spatiotemporal Electricity Emissions for Carbon-Aware Computational Orchestration","display_name":"Carbon-Intensity Predictive Computing for Generative AI: Forecasting Spatiotemporal Electricity Emissions for Carbon-Aware Computational Orchestration","publication_year":2026,"publication_date":"2026-11-06","ids":{"openalex":"https://openalex.org/W7225375608","doi":"https://doi.org/10.5281/zenodo.24001028"},"language":"en","primary_location":{"id":"doi:10.5281/zenodo.24001028","doi":"https://doi.org/10.5281/zenodo.24001028","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24001028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"volume":null,"type":"preprint","is_traditional":true,"indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.5281/zenodo.24001028","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5100638045","display_name":"Jincheng Zhang","orcid":"https://orcid.org/0000-0001-7332-6704","observed_orcids":["https://orcid.org/0000-0001-7332-6704","https://orcid.org/0009-0005-1860-0009"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Zhang, Jincheng","raw_affiliation_strings":[],"raw_orcid":"https://orcid.org/0009-0005-1860-0009","affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5100638045"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12238","display_name":"Green IT and Sustainability","score":0.41769999265670776,"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/T12238","display_name":"Green IT and Sustainability","score":0.41769999265670776,"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/T14098","display_name":"Energy Efficiency in Computing","score":0.4120999872684479,"subfield":{"id":"https://openalex.org/subfields/1708","display_name":"Hardware and Architecture"},"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.14949999749660492,"subfield":{"id":"https://openalex.org/subfields/1710","display_name":"Information Systems"},"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/generative-ai","display_name":"generative AI","score":0.6740000247955322},{"id":"https://openalex.org/keywords/carbon-intensity-forecasting","display_name":"carbon intensity forecasting","score":0.6520000100135803},{"id":"https://openalex.org/keywords/electricity-emissions","display_name":"electricity emissions","score":0.6570000052452087},{"id":"https://openalex.org/keywords/large-language-model-inference","display_name":"large language model inference","score":0.7590000033378601},{"id":"https://openalex.org/keywords/carbon-aware-scheduling","display_name":"carbon-aware scheduling","score":0.6259999871253967},{"id":"https://openalex.org/keywords/carbon-accounting","display_name":"carbon accounting","score":0.8690000176429749},{"id":"https://openalex.org/keywords/workload-scheduling","display_name":"workload scheduling","score":0.503000020980835},{"id":"https://openalex.org/keywords/energy-efficiency","display_name":"energy efficiency","score":0.4309999942779541},{"id":"https://openalex.org/keywords/carbon-emission-reduction","display_name":"carbon emission reduction","score":0.4480000138282776},{"id":"https://openalex.org/keywords/spatiotemporal-prediction","display_name":"spatiotemporal prediction","score":0.7409999966621399},{"id":"https://openalex.org/keywords/gpu-power-consumption","display_name":"GPU power consumption","score":0.574999988079071}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6570000052452087},{"id":"https://openalex.org/C206658404","wikidata":"https://www.wikidata.org/wiki/Q12725","display_name":"Electricity","level":2,"score":0.6445000171661377},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4575999975204468},{"id":"https://openalex.org/C423512","wikidata":"https://www.wikidata.org/wiki/Q383973","display_name":"Electricity generation","level":3,"score":0.4074999988079071},{"id":"https://openalex.org/C199168358","wikidata":"https://www.wikidata.org/wiki/Q3367000","display_name":"Orchestration","level":3,"score":0.3874000012874603},{"id":"https://openalex.org/C188573790","wikidata":"https://www.wikidata.org/wiki/Q12705","display_name":"Renewable energy","level":2,"score":0.37880000472068787},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.33899998664855957},{"id":"https://openalex.org/C146733006","wikidata":"https://www.wikidata.org/wiki/Q676081","display_name":"Electricity market","level":3,"score":0.3156000077724457},{"id":"https://openalex.org/C47737302","wikidata":"https://www.wikidata.org/wiki/Q167336","display_name":"Greenhouse gas","level":2,"score":0.29179999232292175},{"id":"https://openalex.org/C544738498","wikidata":"https://www.wikidata.org/wiki/Q861135","display_name":"Distributed generation","level":3,"score":0.28940001130104065},{"id":"https://openalex.org/C2742236","wikidata":"https://www.wikidata.org/wiki/Q924713","display_name":"Efficient energy use","level":2,"score":0.28790000081062317},{"id":"https://openalex.org/C120314980","wikidata":"https://www.wikidata.org/wiki/Q180634","display_name":"Distributed computing","level":1,"score":0.28279998898506165},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.2793999910354614},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C2992120494","wikidata":"https://www.wikidata.org/wiki/Q27137","display_name":"Electricity system","level":4,"score":0.2685999870300293},{"id":"https://openalex.org/C2778476105","wikidata":"https://www.wikidata.org/wiki/Q628539","display_name":"Workload","level":2,"score":0.26739999651908875},{"id":"https://openalex.org/C134560507","wikidata":"https://www.wikidata.org/wiki/Q753291","display_name":"Environmental economics","level":1,"score":0.2524999976158142}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.5281/zenodo.24001028","doi":"https://doi.org/10.5281/zenodo.24001028","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24001028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},{"id":"doi:10.5281/zenodo.24001029","doi":"https://doi.org/10.5281/zenodo.24001029","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24001029","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.5281/zenodo.24001028","doi":"https://doi.org/10.5281/zenodo.24001028","is_oa":true,"landing_page_url":"https://doi.org/10.5281/zenodo.24001028","pdf_url":null,"source":{"id":"https://openalex.org/S4306400562","display_name":"Zenodo (CERN European Organization for Nuclear Research)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I67311998","host_organization_name":"European Organization for Nuclear Research","host_organization_lineage":["https://openalex.org/I67311998"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":1.0,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"},{"score":0.75,"display_name":"Affordable and clean energy","id":"https://metadata.un.org/sdg/7"},{"score":0.5378999710083008,"display_name":"Industry, innovation and infrastructure","id":"https://metadata.un.org/sdg/9"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W7225427143","https://openalex.org/W4412495078","https://openalex.org/W4402967602","https://openalex.org/W7225405760","https://openalex.org/W7218281818","https://openalex.org/W7221809517","https://openalex.org/W7164888084","https://openalex.org/W7154280409","https://openalex.org/W7225384738","https://openalex.org/W7204435215"],"abstract_inverted_index":{"The":[0,185,225,361,439],"increasing":[1],"deployment":[2],"of":[3,52,70,145,273,367,463],"generative":[4,468],"artificial":[5],"intelligence":[6],"(AI)":[7],"systems":[8],"has":[9],"created":[10],"substantial":[11],"electricity":[12,61,72,90,101,147,166,194,258,261,275,342,453],"demand":[13,40],"associated":[14],"with":[15,106,339,412],"large":[16],"language":[17],"model":[18,21],"(LLM)":[19],"inference,":[20,414],"loading,":[22],"prompt":[23],"processing,":[24],"autoregressive":[25],"generation,":[26],"and":[27,41,84,100,119,171,180,217,222,235,248,310,324,337,352,408,420,451],"distributed":[28,182,214,370],"computational":[29,39,75,104,140,174,215,266,311,348,384,404],"services.":[30],"Although":[31],"predictive":[32,139,209,322,418,464],"computing":[33,465],"provides":[34,442],"opportunities":[35],"to":[36,89,251,283,318,374,394],"anticipate":[37],"future":[38,164],"prepare":[42],"reusable":[43,351],"resources":[44,372],"before":[45],"actual":[46],"requests":[47],"arrive,":[48],"the":[49,67,71,74,143,270,274,319,365,460],"environmental":[50,245,271,461],"consequences":[51],"these":[53,241],"operations":[54],"depend":[55],"not":[56,356],"only":[57],"on":[58,66,117,154,383],"how":[59],"much":[60],"is":[62,281,316,392],"consumed":[63],"but":[64],"also":[65,363],"carbon":[68,78,148,158,204,220,230,262,287,343,377],"intensity":[69,79,159,231,378],"supplying":[73],"infrastructure.":[76],"Electricity":[77],"varies":[80],"across":[81],"geographical":[82,304],"regions":[83],"changes":[85],"over":[86],"time":[87],"according":[88,373],"generation":[91],"composition,":[92],"renewable":[93],"energy":[94,108,267,293],"availability,":[95],"grid":[96,229],"conditions,":[97],"weather":[98],"patterns,":[99],"demand.":[102],"Consequently,":[103],"workloads":[105,329],"identical":[107],"requirements":[109],"may":[110,249,330],"produce":[111],"substantially":[112],"different":[113,244,252],"operational":[114,286,402],"emissions":[115,167,288],"depending":[116],"where":[118,450],"when":[120,452],"they":[121],"are":[122,424,433],"executed.":[123],"This":[124],"paper":[125],"introduces":[126],"Carbon-Intensity":[127],"Predictive":[128],"Computing":[129],"for":[130,291,446,467],"Generative":[131],"AI":[132],"(CIP-GAI),":[133],"a":[134,443],"conceptual":[135,437],"framework":[136,226,362,441],"that":[137,240,346],"extends":[138],"orchestration":[141],"through":[142],"forecasting":[144,397,449],"spatiotemporal":[146,199],"intensity.":[149],"Rather":[150],"than":[151,380],"relying":[152,381],"exclusively":[153,382],"historical":[155],"or":[156,386,429],"current":[157],"measurements,":[160],"CIP-GAI":[161],"investigates":[162,364],"whether":[163,448],"regional":[165,193,295,376,399],"can":[168,458],"be":[169,331,455],"predicted":[170,375],"incorporated":[172],"into":[173],"preparation,":[175,211],"workload":[176,210,300],"scheduling,":[177,416],"GPU":[178,292,371],"allocation,":[179],"geographically":[181,213,369],"inference":[183,328],"decisions.":[184,254],"proposed":[186,393,440],"architecture":[187],"integrates":[188],"six":[189],"principal":[190],"functions:":[191],"(1)":[192],"carbon-intensity":[195,200,296,396],"data":[196],"acquisition,":[197],"(2)":[198],"forecasting,":[201],"(3)":[202],"uncertainty-aware":[203],"emission":[205,431],"estimation,":[206],"(4)":[207],"carbon-aware":[208,325,422],"(5)":[212],"orchestration,":[216],"(6)":[218],"continuous":[219],"accounting":[221,246,290],"adaptive":[223],"optimization.":[224],"distinguishes":[227,257],"average":[228],"from":[232,260,269],"short-run":[233],"marginal":[234,237],"long-run":[236],"emissions,":[238,403],"recognizing":[239],"measures":[242],"represent":[243],"perspectives":[247],"lead":[250],"scheduling":[253,400,423],"It":[255],"further":[256],"consumption":[259],"intensity,":[263,344],"thereby":[264],"separating":[265],"efficiency":[268,462],"characteristics":[272],"supply.":[276],"A":[277],"unified":[278],"optimization":[279],"formulation":[280],"developed":[282],"minimize":[284],"expected":[285],"while":[289],"consumption,":[294],"forecasts,":[297],"prediction":[298],"uncertainty,":[299],"deadlines,":[301],"response":[302],"latency,":[303],"transfer":[305],"costs,":[306],"cloud":[307],"resource":[308],"prices,":[309],"preparation":[312,354],"expenditure.":[313],"Particular":[314],"attention":[315],"given":[317],"interaction":[320],"between":[321],"computation":[323],"execution.":[326],"Anticipated":[327],"prepared":[332],"in":[333,435],"advance":[334],"during":[335],"periods":[336],"locations":[338],"comparatively":[340],"favorable":[341],"provided":[345],"their":[347,353],"states":[349],"remain":[350],"does":[355],"compromise":[357],"immediate":[358],"service":[359],"requirements.":[360],"possibility":[366],"selecting":[368],"rather":[379],"availability":[385],"monetary":[387],"cost.":[388],"An":[389],"experimental":[390,427],"methodology":[391],"evaluate":[395],"accuracy,":[398],"decisions,":[401],"efficiency,":[405],"economic":[406],"expenditure,":[407],"service-level":[409],"performance.":[410],"Comparisons":[411],"reactive":[413],"energy-aware":[415],"price-aware":[417],"computing,":[419],"non-predictive":[421],"established.":[425],"No":[426],"results":[428],"verified":[430],"reductions":[432],"claimed":[434],"this":[436],"study.":[438],"theoretical":[444],"foundation":[445],"investigating":[447],"will":[454],"relatively":[456],"carbon-intensive":[457],"improve":[459],"infrastructures":[466],"AI.":[469]},"counts_by_year":[],"updated_date":"2026-11-08T12:12:28.789561","created_date":"2026-11-07T00:00:00"}