{"id":"https://openalex.org/W2504032317","doi":"https://doi.org/10.1145/2851613.2853124","title":"Sensor network based solar forecasting using a local vector autoregressive ridge framework","display_name":"Sensor network based solar forecasting using a local vector autoregressive ridge framework","publication_year":2016,"publication_date":"2016-04-04","ids":{"openalex":"https://openalex.org/W2504032317","doi":"https://doi.org/10.1145/2851613.2853124","mag":"2504032317"},"language":"en","primary_location":{"id":"doi:10.1145/2851613.2853124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2851613.2853124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st Annual ACM Symposium on Applied Computing","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/A5029470370","display_name":"Jin Xu","orcid":"https://orcid.org/0000-0001-9193-7648"},"institutions":[{"id":"https://openalex.org/I59553526","display_name":"Stony Brook University","ror":"https://ror.org/05qghxh33","country_code":"US","type":"education","lineage":["https://openalex.org/I59553526"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Jin Xu","raw_affiliation_strings":["Stony Brook University"],"affiliations":[{"raw_affiliation_string":"Stony Brook University","institution_ids":["https://openalex.org/I59553526"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5048176207","display_name":"Shinjae Yoo","orcid":"https://orcid.org/0000-0003-4378-6448"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shinjae Yoo","raw_affiliation_strings":["Brookhaven National Lab"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Lab","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070321735","display_name":"John Heiser","orcid":"https://orcid.org/0000-0001-7444-2045"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"John Heiser","raw_affiliation_strings":["Brookhaven National Lab"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Lab","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5081281378","display_name":"P.D. Kalb","orcid":"https://orcid.org/0000-0002-0093-0430"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Paul Kalb","raw_affiliation_strings":["Brookhaven National Lab"],"affiliations":[{"raw_affiliation_string":"Brookhaven National Lab","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5029470370"],"corresponding_institution_ids":["https://openalex.org/I59553526"],"apc_list":null,"apc_paid":null,"fwci":1.7139,"has_fulltext":false,"cited_by_count":8,"citation_normalized_percentile":{"value":0.88965469,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":null,"issue":null,"first_page":"2113","last_page":"2118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11276","display_name":"Solar Radiation and Photovoltaics","score":1.0,"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":1.0,"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/T10468","display_name":"Photovoltaic System Optimization Techniques","score":0.9979000091552734,"subfield":{"id":"https://openalex.org/subfields/2105","display_name":"Renewable Energy, Sustainability and the Environment"},"field":{"id":"https://openalex.org/fields/21","display_name":"Energy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9921000003814697,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/solar-irradiance","display_name":"Solar irradiance","score":0.6837400197982788},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.59339439868927},{"id":"https://openalex.org/keywords/irradiance","display_name":"Irradiance","score":0.5827612280845642},{"id":"https://openalex.org/keywords/cloud-computing","display_name":"Cloud computing","score":0.5504350662231445},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.5041767358779907},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.49115726351737976},{"id":"https://openalex.org/keywords/photovoltaic-system","display_name":"Photovoltaic system","score":0.4786859154701233},{"id":"https://openalex.org/keywords/overcast","display_name":"Overcast","score":0.46689116954803467},{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.46159929037094116},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.4390266239643097},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4307847023010254},{"id":"https://openalex.org/keywords/environmental-science","display_name":"Environmental science","score":0.41223737597465515},{"id":"https://openalex.org/keywords/sky","display_name":"Sky","score":0.2403639554977417},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.23580902814865112},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.20706218481063843},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.19020116329193115},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.16468167304992676},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.13704821467399597},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.12667807936668396},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.11586248874664307}],"concepts":[{"id":"https://openalex.org/C9695528","wikidata":"https://www.wikidata.org/wiki/Q7556707","display_name":"Solar irradiance","level":2,"score":0.6837400197982788},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.59339439868927},{"id":"https://openalex.org/C46423501","wikidata":"https://www.wikidata.org/wiki/Q830654","display_name":"Irradiance","level":2,"score":0.5827612280845642},{"id":"https://openalex.org/C79974875","wikidata":"https://www.wikidata.org/wiki/Q483639","display_name":"Cloud computing","level":2,"score":0.5504350662231445},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.5041767358779907},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.49115726351737976},{"id":"https://openalex.org/C41291067","wikidata":"https://www.wikidata.org/wiki/Q1897785","display_name":"Photovoltaic system","level":2,"score":0.4786859154701233},{"id":"https://openalex.org/C195452092","wikidata":"https://www.wikidata.org/wiki/Q1055865","display_name":"Overcast","level":3,"score":0.46689116954803467},{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.46159929037094116},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.4390266239643097},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4307847023010254},{"id":"https://openalex.org/C39432304","wikidata":"https://www.wikidata.org/wiki/Q188847","display_name":"Environmental science","level":0,"score":0.41223737597465515},{"id":"https://openalex.org/C73329638","wikidata":"https://www.wikidata.org/wiki/Q527","display_name":"Sky","level":2,"score":0.2403639554977417},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.23580902814865112},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.20706218481063843},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.19020116329193115},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.16468167304992676},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13704821467399597},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.12667807936668396},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.11586248874664307},{"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},{"id":"https://openalex.org/C111919701","wikidata":"https://www.wikidata.org/wiki/Q9135","display_name":"Operating system","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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/2851613.2853124","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2851613.2853124","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 31st Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/7","display_name":"Affordable and clean energy","score":0.5899999737739563}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1984703120","https://openalex.org/W1989169134","https://openalex.org/W2001464819","https://openalex.org/W2005745539","https://openalex.org/W2021707878","https://openalex.org/W2042511026","https://openalex.org/W2046890513","https://openalex.org/W2051403129","https://openalex.org/W2056192250","https://openalex.org/W2112497712","https://openalex.org/W2140732669","https://openalex.org/W2203746523","https://openalex.org/W2530626634"],"related_works":["https://openalex.org/W2504838903","https://openalex.org/W3104379783","https://openalex.org/W2016093451","https://openalex.org/W1530649511","https://openalex.org/W3106902037","https://openalex.org/W2131174359","https://openalex.org/W4322211974","https://openalex.org/W2104775196","https://openalex.org/W2884263184","https://openalex.org/W2995848355"],"abstract_inverted_index":{"The":[0,77],"significant":[1],"improvements":[2],"and":[3,48,64,143,168],"falling":[4],"costs":[5],"of":[6,21,80,89,151,165],"photovoltaic":[7],"(PV)":[8],"technology":[9],"make":[10],"solar":[11,23],"energy":[12],"a":[13,87,108,182],"promising":[14],"resource,":[15],"yet":[16],"the":[17,38,55,122,149,154,173],"cloud":[18,104,126,141],"induced":[19],"variability":[20],"surface":[22],"irradiance":[24,34,60,90,118],"inhibits":[25],"its":[26],"effective":[27],"use":[28],"in":[29,103],"grid-tied":[30],"PV":[31],"generation.":[32],"Short-term":[33],"forecasting,":[35],"especially":[36],"on":[37,86,181],"minute":[39,179],"scale,":[40],"is":[41,92,137],"critically":[42],"important":[43],"for":[44,177],"grid":[45],"system":[46],"stability":[47],"auxiliary":[49],"power":[50],"source":[51],"management.":[52],"Compared":[53],"to":[54,66,93,100,116,139],"trending":[56],"sky":[57],"imaging":[58],"devices,":[59],"sensors":[61,91],"are":[62],"inexpensive":[63],"easy":[65],"deploy":[67],"but":[68],"related":[69],"forecasting":[70],"methods":[71],"have":[72],"not":[73],"been":[74],"well":[75],"researched.":[76],"prominent":[78],"challenge":[79],"applying":[81],"classic":[82],"time":[83],"series":[84],"models":[85],"network":[88],"address":[94],"their":[95],"varying":[96],"spatio-temporal":[97],"correlations":[98],"due":[99],"local":[101,109,130,140],"changes":[102],"conditions.":[105],"We":[106],"propose":[107],"vector":[110],"autoregressive":[111],"framework":[112],"with":[113],"ridge":[114],"regularization":[115],"forecast":[117,135],"without":[119],"explicitly":[120],"determining":[121],"wind":[123],"field":[124],"or":[125],"movement.":[127],"By":[128],"using":[129,145],"training":[131,156],"data,":[132],"our":[133],"learned":[134],"model":[136],"adaptive":[138],"conditions":[142],"by":[144],"regularization,":[146],"we":[147],"overcome":[148],"risk":[150],"overfitting":[152],"from":[153],"limited":[155],"data.":[157],"Our":[158],"systematic":[159],"experimental":[160],"results":[161],"showed":[162],"an":[163],"average":[164],"19.7%":[166],"RMSE":[167],"20.2%":[169],"MAE":[170],"improvement":[171],"over":[172],"benchmark":[174],"Persistent":[175],"Model":[176],"1-5":[178],"forecasts":[180],"comprehensive":[183],"25-day":[184],"dataset.":[185]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2017,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}