{"id":"https://openalex.org/W2811193345","doi":"https://doi.org/10.1109/uic-atc.2017.8397649","title":"A CNN based bagging learning approach to short-term load forecasting in smart grid","display_name":"A CNN based bagging learning approach to short-term load forecasting in smart grid","publication_year":2017,"publication_date":"2017-08-01","ids":{"openalex":"https://openalex.org/W2811193345","doi":"https://doi.org/10.1109/uic-atc.2017.8397649","mag":"2811193345"},"language":"en","primary_location":{"id":"doi:10.1109/uic-atc.2017.8397649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uic-atc.2017.8397649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)","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/A5006284276","display_name":"Xishuang Dong","orcid":"https://orcid.org/0000-0002-3742-0071"},"institutions":[{"id":"https://openalex.org/I173268674","display_name":"Texas A&M University System","ror":"https://ror.org/0034eay46","country_code":"US","type":"education","lineage":["https://openalex.org/I173268674"]},{"id":"https://openalex.org/I250520410","display_name":"Prairie View A&M University","ror":"https://ror.org/0449kf092","country_code":"US","type":"education","lineage":["https://openalex.org/I250520410"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Xishuang Dong","raw_affiliation_strings":["Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA","institution_ids":["https://openalex.org/I250520410","https://openalex.org/I173268674"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5085031765","display_name":"Lijun Qian","orcid":"https://orcid.org/0000-0003-1577-3359"},"institutions":[{"id":"https://openalex.org/I173268674","display_name":"Texas A&M University System","ror":"https://ror.org/0034eay46","country_code":"US","type":"education","lineage":["https://openalex.org/I173268674"]},{"id":"https://openalex.org/I250520410","display_name":"Prairie View A&M University","ror":"https://ror.org/0449kf092","country_code":"US","type":"education","lineage":["https://openalex.org/I250520410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lijun Qian","raw_affiliation_strings":["Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA","institution_ids":["https://openalex.org/I250520410","https://openalex.org/I173268674"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5041982864","display_name":"Lei Huang","orcid":"https://orcid.org/0000-0003-0018-0569"},"institutions":[{"id":"https://openalex.org/I173268674","display_name":"Texas A&M University System","ror":"https://ror.org/0034eay46","country_code":"US","type":"education","lineage":["https://openalex.org/I173268674"]},{"id":"https://openalex.org/I250520410","display_name":"Prairie View A&M University","ror":"https://ror.org/0449kf092","country_code":"US","type":"education","lineage":["https://openalex.org/I250520410"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lei Huang","raw_affiliation_strings":["Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA"],"affiliations":[{"raw_affiliation_string":"Center of Excellence in Research and Education for Big Military Data Intelligence, Texas A&M University System, Prairie View, TX, USA","institution_ids":["https://openalex.org/I250520410","https://openalex.org/I173268674"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5006284276"],"corresponding_institution_ids":["https://openalex.org/I173268674","https://openalex.org/I250520410"],"apc_list":null,"apc_paid":null,"fwci":2.8669,"has_fulltext":false,"cited_by_count":49,"citation_normalized_percentile":{"value":0.91640753,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.9997000098228455,"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":0.9997000098228455,"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/T10688","display_name":"Image and Signal Denoising Methods","score":0.9951000213623047,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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":0.9915000200271606,"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/computer-science","display_name":"Computer science","score":0.7417834997177124},{"id":"https://openalex.org/keywords/smart-grid","display_name":"Smart grid","score":0.6373376846313477},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.5703465342521667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5455926060676575},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5413228869438171},{"id":"https://openalex.org/keywords/grid","display_name":"Grid","score":0.5196853280067444},{"id":"https://openalex.org/keywords/scheduling","display_name":"Scheduling (production processes)","score":0.5099540948867798},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.5082849860191345},{"id":"https://openalex.org/keywords/spark","display_name":"SPARK (programming language)","score":0.49334684014320374},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.47436508536338806},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.45846566557884216},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.45546942949295044},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.44944432377815247},{"id":"https://openalex.org/keywords/electrical-load","display_name":"Electrical load","score":0.44242310523986816},{"id":"https://openalex.org/keywords/big-data","display_name":"Big data","score":0.44208788871765137},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.421461820602417},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.39932775497436523},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.1299593150615692}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7417834997177124},{"id":"https://openalex.org/C10558101","wikidata":"https://www.wikidata.org/wiki/Q689855","display_name":"Smart grid","level":2,"score":0.6373376846313477},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.5703465342521667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5455926060676575},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5413228869438171},{"id":"https://openalex.org/C187691185","wikidata":"https://www.wikidata.org/wiki/Q2020720","display_name":"Grid","level":2,"score":0.5196853280067444},{"id":"https://openalex.org/C206729178","wikidata":"https://www.wikidata.org/wiki/Q2271896","display_name":"Scheduling (production processes)","level":2,"score":0.5099540948867798},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.5082849860191345},{"id":"https://openalex.org/C2781215313","wikidata":"https://www.wikidata.org/wiki/Q3493345","display_name":"SPARK (programming language)","level":2,"score":0.49334684014320374},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.47436508536338806},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.45846566557884216},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.45546942949295044},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.44944432377815247},{"id":"https://openalex.org/C77715397","wikidata":"https://www.wikidata.org/wiki/Q931447","display_name":"Electrical load","level":3,"score":0.44242310523986816},{"id":"https://openalex.org/C75684735","wikidata":"https://www.wikidata.org/wiki/Q858810","display_name":"Big data","level":2,"score":0.44208788871765137},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.421461820602417},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.39932775497436523},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.1299593150615692},{"id":"https://openalex.org/C165801399","wikidata":"https://www.wikidata.org/wiki/Q25428","display_name":"Voltage","level":2,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"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/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","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/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/uic-atc.2017.8397649","is_oa":false,"landing_page_url":"https://doi.org/10.1109/uic-atc.2017.8397649","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE SmartWorld, Ubiquitous Intelligence & Computing, Advanced & Trusted Computed, Scalable Computing & Communications, Cloud & Big Data Computing, Internet of People and Smart City Innovation (SmartWorld/SCALCOM/UIC/ATC/CBDCom/IOP/SCI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/9","score":0.5899999737739563,"display_name":"Industry, innovation and infrastructure"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":36,"referenced_works":["https://openalex.org/W97016928","https://openalex.org/W1988608305","https://openalex.org/W2001165499","https://openalex.org/W2003399840","https://openalex.org/W2007272376","https://openalex.org/W2008725920","https://openalex.org/W2016053056","https://openalex.org/W2016210396","https://openalex.org/W2023124966","https://openalex.org/W2041387425","https://openalex.org/W2046933993","https://openalex.org/W2099866409","https://openalex.org/W2106262311","https://openalex.org/W2122585011","https://openalex.org/W2123007178","https://openalex.org/W2127192673","https://openalex.org/W2135827766","https://openalex.org/W2139073438","https://openalex.org/W2144335970","https://openalex.org/W2150783679","https://openalex.org/W2151767444","https://openalex.org/W2163605009","https://openalex.org/W2168138569","https://openalex.org/W2172064003","https://openalex.org/W2293634267","https://openalex.org/W2295959395","https://openalex.org/W2522565601","https://openalex.org/W2599285715","https://openalex.org/W2603648311","https://openalex.org/W2604352584","https://openalex.org/W2919115771","https://openalex.org/W3122232506","https://openalex.org/W3125653406","https://openalex.org/W4297790949","https://openalex.org/W6603960082","https://openalex.org/W6684191040"],"related_works":["https://openalex.org/W2766461310","https://openalex.org/W4247566972","https://openalex.org/W3202731209","https://openalex.org/W4240326769","https://openalex.org/W3211874991","https://openalex.org/W3012518171","https://openalex.org/W2906268655","https://openalex.org/W2898021358","https://openalex.org/W4241605045","https://openalex.org/W2899494475"],"abstract_inverted_index":{"Short-term":[0],"load":[1,46,55,97,122],"forecasting":[2,34,42,63,71,77,83],"in":[3,99,115,121],"smart":[4],"grid":[5],"is":[6],"key":[7],"to":[8,40,68,79,109],"electricity":[9],"dispatch":[10],"scheduling,":[11],"reliability":[12],"analysis,":[13],"and":[14,73,88,117],"maintenance":[15],"planning":[16],"for":[17,33],"the":[18,62,86,127,130],"generators.":[19],"In":[20],"this":[21],"paper,":[22],"we":[23,50],"present":[24],"a":[25,52,81],"convolutional":[26],"neural":[27],"networks":[28],"(CNN)":[29],"based":[30],"bagging":[31,82],"model":[32],"hourly":[35],"loads.":[36],"We":[37],"employ":[38],"CNN":[39],"train":[41],"models":[43,64,78],"on":[44,65,93],"big":[45],"data":[47,56,101],"sets.":[48],"Then,":[49],"segment":[51],"real":[53],"industry":[54],"set":[57],"into":[58],"many":[59],"subsets,":[60],"fine-tune":[61],"these":[66,75],"subsets":[67],"learn":[69],"weak":[70,76],"models,":[72],"assemble":[74],"conduct":[80],"model,":[84],"where":[85],"learning":[87],"assembling":[89],"procedures":[90],"are":[91,103],"implemented":[92],"Spark.":[94],"Specifically,":[95],"all":[96],"samples":[98],"those":[100,118],"sets":[102],"reorganized":[104],"as":[105],"images":[106,116],"with":[107],"respect":[108],"similarities":[110],"between":[111],"relations":[112],"of":[113,119,129],"pixels":[114],"features":[120],"samples.":[123],"Experimental":[124],"results":[125],"indicate":[126],"effectiveness":[128],"proposed":[131],"method.":[132]},"counts_by_year":[{"year":2025,"cited_by_count":2},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":6},{"year":2022,"cited_by_count":7},{"year":2021,"cited_by_count":7},{"year":2020,"cited_by_count":14},{"year":2019,"cited_by_count":5},{"year":2018,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}