{"id":"https://openalex.org/W7160070034","doi":"https://doi.org/10.48550/arxiv.2605.00681","title":"Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation","display_name":"Deployment-Efficient Short-Term Load Forecasting in AI Data Centers via Sequence-to-Point Knowledge Distillation","publication_year":2026,"publication_date":"2026-05-01","ids":{"openalex":"https://openalex.org/W7160070034","doi":"https://doi.org/10.48550/arxiv.2605.00681"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2605.00681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2605.00681","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5135193137","display_name":"Lei Wang","orcid":"https://orcid.org/0009-0006-3236-867X"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Lei","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135179493","display_name":"Jiahao Chen","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Chen, Jiahao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018927509","display_name":"Fanping Sui","orcid":"https://orcid.org/0009-0004-4040-0639"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sui, Fanping","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5135274274","display_name":"Ying Zhang","orcid":"https://orcid.org/0000-0002-8299-3848"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Zhang, Ying","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5135118799","display_name":"Di Shi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shi, Di","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.35830000042915344,"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":0.35830000042915344,"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/T10101","display_name":"Cloud Computing and Resource Management","score":0.29600000381469727,"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"}},{"id":"https://openalex.org/T11344","display_name":"Traffic Prediction and Management Techniques","score":0.06069999933242798,"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/robustness","display_name":"Robustness (evolution)","score":0.6047999858856201},{"id":"https://openalex.org/keywords/train","display_name":"Train","score":0.567300021648407},{"id":"https://openalex.org/keywords/residual","display_name":"Residual","score":0.5659000277519226},{"id":"https://openalex.org/keywords/software-deployment","display_name":"Software deployment","score":0.5625},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.560699999332428},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5437999963760376},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.41990000009536743},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.3995000123977661},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.35420000553131104}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7276999950408936},{"id":"https://openalex.org/C63479239","wikidata":"https://www.wikidata.org/wiki/Q7353546","display_name":"Robustness (evolution)","level":3,"score":0.6047999858856201},{"id":"https://openalex.org/C190839683","wikidata":"https://www.wikidata.org/wiki/Q2448197","display_name":"Train","level":2,"score":0.567300021648407},{"id":"https://openalex.org/C155512373","wikidata":"https://www.wikidata.org/wiki/Q287450","display_name":"Residual","level":2,"score":0.5659000277519226},{"id":"https://openalex.org/C105339364","wikidata":"https://www.wikidata.org/wiki/Q2297740","display_name":"Software deployment","level":2,"score":0.5625},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.560699999332428},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5437999963760376},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5292999744415283},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5285000205039978},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.41990000009536743},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.3995000123977661},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.37049999833106995},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.35420000553131104},{"id":"https://openalex.org/C150899416","wikidata":"https://www.wikidata.org/wiki/Q1820378","display_name":"Transfer of learning","level":2,"score":0.352400004863739},{"id":"https://openalex.org/C13662910","wikidata":"https://www.wikidata.org/wiki/Q193139","display_name":"Trajectory","level":2,"score":0.34290000796318054},{"id":"https://openalex.org/C175291020","wikidata":"https://www.wikidata.org/wiki/Q1156822","display_name":"Offset (computer science)","level":2,"score":0.3395000100135803},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.32510000467300415},{"id":"https://openalex.org/C193809577","wikidata":"https://www.wikidata.org/wiki/Q3409300","display_name":"Demand forecasting","level":2,"score":0.3228999972343445},{"id":"https://openalex.org/C74912251","wikidata":"https://www.wikidata.org/wiki/Q6815727","display_name":"Memory footprint","level":2,"score":0.321399986743927},{"id":"https://openalex.org/C153740404","wikidata":"https://www.wikidata.org/wiki/Q671224","display_name":"Data center","level":2,"score":0.3203999996185303},{"id":"https://openalex.org/C89227174","wikidata":"https://www.wikidata.org/wiki/Q2388981","display_name":"Electric power system","level":3,"score":0.3118000030517578},{"id":"https://openalex.org/C79403827","wikidata":"https://www.wikidata.org/wiki/Q3988","display_name":"Real-time computing","level":1,"score":0.2962999939918518},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.29100000858306885},{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.28369998931884766},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2662999927997589},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.2646999955177307},{"id":"https://openalex.org/C82876162","wikidata":"https://www.wikidata.org/wiki/Q17096504","display_name":"Latency (audio)","level":2,"score":0.25780001282691956},{"id":"https://openalex.org/C132943942","wikidata":"https://www.wikidata.org/wiki/Q2562511","display_name":"Footprint","level":2,"score":0.2567000091075897},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.2556999921798706},{"id":"https://openalex.org/C2776960227","wikidata":"https://www.wikidata.org/wiki/Q2586354","display_name":"Knowledge transfer","level":2,"score":0.2556000053882599},{"id":"https://openalex.org/C204030448","wikidata":"https://www.wikidata.org/wiki/Q101017","display_name":"Distillation","level":2,"score":0.25119999051094055}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2605.00681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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:10.48550/arxiv.2605.00681","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.00681","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"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":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"forecasting":[1,37,79,168],"the":[2,21,134,137,157,163,177],"bursty":[3],"and":[4,31,50,150,186],"non-stationary":[5,108],"power":[6,29],"demand":[7],"of":[8,47],"AI":[9,81],"data":[10,82],"centers":[11],"has":[12],"become":[13],"increasingly":[14],"important,":[15],"as":[16],"abrupt":[17],"workload-driven":[18],"variations":[19],"at":[20,44],"GPU-node":[22],"level":[23],"can":[24],"affect":[25],"real-time":[26],"operational":[27],"efficiency,":[28],"management,":[30],"grid-data":[32],"center":[33],"coordination.":[34],"However,":[35],"high-capacity":[36,90],"models":[38],"are":[39],"often":[40],"difficult":[41],"to":[42,58,104,136],"deploy":[43],"scale":[45],"because":[46],"their":[48],"memory":[49,185],"latency":[51],"requirements,":[52],"while":[53,175],"lightweight":[54,112],"predictors":[55],"may":[56],"fail":[57],"capture":[59],"short-horizon":[60],"temporal":[61,131],"dynamics.":[62],"To":[63,129],"address":[64],"this":[65,68],"accuracy-deployment":[66],"tradeoff,":[67],"paper":[69],"proposes":[70],"a":[71,89,124,139],"deployment-efficient":[72],"knowledge":[73,132],"distillation":[74,141],"framework":[75,86],"for":[76,94,119],"short-term":[77],"load":[78,96],"in":[80,183],"centers.":[83],"The":[84],"proposed":[85,164],"first":[87],"trains":[88],"sequence":[91],"teacher":[92,135],"model":[93,115,166,187],"multi-step":[95],"trajectory":[97],"prediction,":[98],"where":[99],"residual":[100],"learning":[101,173],"is":[102,116,143],"used":[103],"improve":[105],"robustness":[106],"under":[107],"operating":[109],"conditions.":[110],"A":[111],"point-wise":[113],"student":[114,165],"then":[117],"developed":[118],"low-latency":[120],"rolling":[121],"inference":[122],"using":[123],"compact":[125],"neural":[126],"network":[127],"architecture.":[128],"transfer":[130],"from":[133],"student,":[138],"sequence-to-point":[140],"strategy":[142],"introduced":[144],"by":[145,180],"aligning":[146],"near-term":[147],"predictive":[148],"behavior":[149],"temporally":[151],"pooled":[152],"representations.":[153],"Case":[154],"studies":[155],"on":[156],"MIT":[158],"Supercloud":[159],"dataset":[160],"demonstrate":[161],"that":[162],"improves":[167],"accuracy":[169],"over":[170,181],"recent":[171],"deep":[172],"baselines":[174],"reducing":[176],"deployment":[178],"footprint":[179],"10x":[182],"parameter":[184],"size.":[188]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-05-05T00:00:00"}